cpu utilization - Page 60

2 partitions in a sharded mysql database

- by ming yeow

the first one is running at 73% memory utilization. The second one runs at 16% utilization. how can i balance the 2 out?

Oracle DB on solaris utilizing swap memory when free RAM available

- by Ara

Hi, We have a weird instance where we noticed our oracle database server swap utilization was 100% and surprised to see that the system had free memory available during that period. To my knowledge, swap memory utilization starts once system runs out of free RAM (please correct me if i'm wrong). Not sure what could have caused this unusual activity. Had anyone else experienced such behaviour? Regs,

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string.format vs + for string concatenatoin

- by AMissico

Which is better in respect to performance and memory utilization? // + Operator oMessage.Subject = "Agreement, # " + sNumber + ", Name: " + sName; // String.Format oMessage.Subject = string.Format("Agreement, # {0}, Name: {1}", sNumber, sName); My preference is memory utilization. The + operator is used throughout the application. String.Format and StringBuilder is rarely use. I want to reduce the amount of memory fragmentation caused by excessive string allocations.

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Built-in GZip/Deflate Compression on IIS 7.x

- by Rick Strahl

IIS 7 improves internal compression functionality dramatically making it much easier than previous versions to take advantage of compression that’s built-in to the Web server. IIS 7 also supports dynamic compression which allows automatic compression of content created in your own applications (ASP.NET or otherwise!). The scheme is based on content-type sniffing and so it works with any kind of Web application framework. While static compression on IIS 7 is super easy to set up and turned on by default for most text content (text/*, which includes HTML and CSS, as well as for JavaScript, Atom, XAML, XML), setting up dynamic compression is a bit more involved, mostly because the various default compression settings are set in multiple places down the IIS –> ASP.NET hierarchy. Let’s take a look at each of the two approaches available: Static Compression Compresses static content from the hard disk. IIS can cache this content by compressing the file once and storing the compressed file on disk and serving the compressed alias whenever static content is requested and it hasn’t changed. The overhead for this is minimal and should be aggressively enabled. Dynamic Compression Works against application generated output from applications like your ASP.NET apps. Unlike static content, dynamic content must be compressed every time a page that requests it regenerates its content. As such dynamic compression has a much bigger impact than static caching. How Compression is configured Compression in IIS 7.x is configured with two .config file elements in the <system.WebServer> space. The elements can be set anywhere in the IIS/ASP.NET configuration pipeline all the way from ApplicationHost.config down to the local web.config file. The following is from the the default setting in ApplicationHost.config (in the %windir%\System32\inetsrv\config forlder) on IIS 7.5 with a couple of small adjustments (added json output and enabled dynamic compression): <?xml version="1.0" encoding="UTF-8"?> <configuration> <system.webServer> <httpCompression directory="%SystemDrive%\inetpub\temp\IIS Temporary Compressed Files"> <scheme name="gzip" dll="%Windir%\system32\inetsrv\gzip.dll" staticCompressionLevel="9" /> <dynamicTypes> <add mimeType="text/*" enabled="true" /> <add mimeType="message/*" enabled="true" /> <add mimeType="application/x-javascript" enabled="true" /> <add mimeType="application/json" enabled="true" /> <add mimeType="*/*" enabled="false" /> </dynamicTypes> <staticTypes> <add mimeType="text/*" enabled="true" /> <add mimeType="message/*" enabled="true" /> <add mimeType="application/x-javascript" enabled="true" /> <add mimeType="application/atom+xml" enabled="true" /> <add mimeType="application/xaml+xml" enabled="true" /> <add mimeType="*/*" enabled="false" /> </staticTypes> </httpCompression> <urlCompression doStaticCompression="true" doDynamicCompression="true" /> </system.webServer> </configuration> You can find documentation on the httpCompression and urlCompression keys here respectively: http://msdn.microsoft.com/en-us/library/ms690689%28v=vs.90%29.aspx http://msdn.microsoft.com/en-us/library/aa347437%28v=vs.90%29.aspx The httpCompression Element – What and How to compress Basically httpCompression configures what types to compress and how to compress them. It specifies the DLL that handles gzip encoding and the types of documents that are to be compressed. Types are set up based on mime-types which looks at returned Content-Type headers in HTTP responses. For example, I added the application/json to mime type to my dynamic compression types above to allow that content to be compressed as well since I have quite a bit of AJAX content that gets sent to the client. The UrlCompression Element – Enables and Disables Compression The urlCompression element is a quick way to turn compression on and off. By default static compression is enabled server wide, and dynamic compression is disabled server wide. This might be a bit confusing because the httpCompression element also has a doDynamicCompression attribute which is set to true by default, but the urlCompression attribute by the same name actually overrides it. The urlCompression element only has three attributes: doStaticCompression, doDynamicCompression and dynamicCompressionBeforeCache. The doCompression attributes are the final determining factor whether compression is enabled, so it’s a good idea to be explcit! The default for doDynamicCompression='false”, but doStaticCompression="true"! Static Compression is enabled by Default, Dynamic Compression is not Because static compression is very efficient in IIS 7 it’s enabled by default server wide and there probably is no reason to ever change that setting. Dynamic compression however, since it’s more resource intensive, is turned off by default. If you want to enable dynamic compression there are a few quirks you have to deal with, namely that enabling it in ApplicationHost.config doesn’t work. Setting: <urlCompression doDynamicCompression="true" /> in applicationhost.config appears to have no effect and I had to move this element into my local web.config to make dynamic compression work. This is actually a smart choice because you’re not likely to want dynamic compression in every application on a server. Rather dynamic compression should be applied selectively where it makes sense. However, nowhere is it documented that the setting in applicationhost.config doesn’t work (or more likely is overridden somewhere and disabled lower in the configuration hierarchy). So: remember to set doDynamicCompression=”true” in web.config!!! How Static Compression works Static compression works against static content loaded from files on disk. Because this content is static and not bound to change frequently – such as .js, .css and static HTML content – it’s fairly easy for IIS to compress and then cache the compressed content. The way this works is that IIS compresses the files into a special folder on the server’s hard disk and then reads the content from this location if already compressed content is requested and the underlying file resource has not changed. The semantics of serving an already compressed file are very efficient – IIS still checks for file changes, but otherwise just serves the already compressed file from the compression folder. The compression folder is located at: %windir%\inetpub\temp\IIS Temporary Compressed Files\ApplicationPool\ If you look into the subfolders you’ll find compressed files: These files are pre-compressed and IIS serves them directly to the client until the underlying files are changed. As I mentioned before – static compression is on by default and there’s very little reason to turn that functionality off as it is efficient and just works out of the box. The one tweak you might want to do is to set the compression level to maximum. Since IIS only compresses content very infrequently it would make sense to apply maximum compression. You can do this with the staticCompressionLevel setting on the scheme element: <scheme name="gzip" dll="%Windir%\system32\inetsrv\gzip.dll" staticCompressionLevel="9" /> Other than that the default settings are probably just fine. Dynamic Compression – not so fast! By default dynamic compression is disabled and that’s actually quite sensible – you should use dynamic compression very carefully and think about what content you want to compress. In most applications it wouldn’t make sense to compress *all* generated content as it would generate a significant amount of overhead. Scott Fortsyth has a great post that details some of the performance numbers and how much impact dynamic compression has. Depending on how busy your server is you can play around with compression and see what impact it has on your server’s performance. There are also a few settings you can tweak to minimize the overhead of dynamic compression. Specifically the httpCompression key has a couple of CPU related keys that can help minimize the impact of Dynamic Compression on a busy server: dynamicCompressionDisableCpuUsage dynamicCompressionEnableCpuUsage By default these are set to 90 and 50 which means that when the CPU hits 90% compression will be disabled until CPU utilization drops back down to 50%. Again this is actually quite sensible as it utilizes CPU power from compression when available and falling off when the threshold has been hit. It’s a good way some of that extra CPU power on your big servers to use when utilization is low. Again these settings are something you likely have to play with. I would probably set the upper limit a little lower than 90% maybe around 70% to make this a feature that kicks in only if there’s lots of power to spare. I’m not really sure how accurate these CPU readings that IIS uses are as Cpu usage on Web Servers can spike drastically even during low loads. Don’t trust settings – do some load testing or monitor your server in a live environment to see what values make sense for your environment. Finally for dynamic compression I tend to add one Mime type for JSON data, since a lot of my applications send large chunks of JSON data over the wire. You can do that with the application/json content type: <add mimeType="application/json" enabled="true" /> What about Deflate Compression? The default compression is GZip. The documentation hints that you can use a different compression scheme and mentions Deflate compression. And sure enough you can change the compression settings to: <scheme name="deflate" dll="%Windir%\system32\inetsrv\gzip.dll" staticCompressionLevel="9" /> to get deflate style compression. The deflate algorithm produces slightly more compact output so I tend to prefer it over GZip but more HTTP clients (other than browsers) support GZip than Deflate so be careful with this option if you build Web APIs. I also had some issues with the above value actually being applied right away. Changing the scheme in applicationhost.config didn’t show up on the site right away. It required me to do a full IISReset to get that change to show up before I saw the change over to deflate compressed content. Content was slightly more compressed with deflate – not sure if it’s worth the slightly less common compression type, but the option at least is available. IIS 7 finally makes GZip Easy In summary IIS 7 makes GZip easy finally, even if the configuration settings are a bit obtuse and the documentation is seriously lacking. But once you know the basic settings I’ve described here and the fact that you can override all of this in your local web.config it’s pretty straight forward to configure GZip support and tweak it exactly to your needs. Static compression is a total no brainer as it adds very little overhead compared to direct static file serving and provides solid compression. Dynamic Compression is a little more tricky as it does add some overhead to servers, so it probably will require some tweaking to get the right balance of CPU load vs. compression ratios. Looking at large sites like Amazon, Yahoo, NewEgg etc. – they all use Related Content Code based ASP.NET GZip Caveats HttpWebRequest and GZip Responses © Rick Strahl, West Wind Technologies, 2005-2011Posted in IIS7 ASP.NET

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Win7 Bluescreen: IRQ_NOT_LESS_OR_EQUAL | athrxusb.sys

- by wretrOvian

Hi I'd left my system on last night, and found the bluescreen in the morning. This has been happening occasionally, over the past few days. Details: ================================================== Dump File : 022710-18236-01.dmp Crash Time : 2/27/2010 8:46:44 AM Bug Check String : DRIVER_IRQL_NOT_LESS_OR_EQUAL Bug Check Code : 0x000000d1 Parameter 1 : 00000000`00001001 Parameter 2 : 00000000`00000002 Parameter 3 : 00000000`00000000 Parameter 4 : fffff880`06b5c0e1 Caused By Driver : athrxusb.sys Caused By Address : athrxusb.sys+760e1 File Description : Product Name : Company : File Version : Processor : x64 Computer Name : Full Path : C:\Windows\minidump\022710-18236-01.dmp Processors Count : 2 Major Version : 15 Minor Version : 7600 ================================================== HiJackThis ("[...]" indicates removed text; full log posted to pastebin): Logfile of Trend Micro HijackThis v2.0.2 Scan saved at 8:49:15 AM, on 2/27/2010 Platform: Unknown Windows (WinNT 6.01.3504) MSIE: Internet Explorer v8.00 (8.00.7600.16385) Boot mode: Normal Running processes: C:\Windows\DAODx.exe C:\Program Files (x86)\ASUS\EPU\EPU.exe C:\Program Files\ASUS\TurboV\TurboV.exe C:\Program Files (x86)\PowerISO\PWRISOVM.EXE C:\Program Files (x86)\OpenOffice.org 3\program\soffice.exe C:\Program Files (x86)\OpenOffice.org 3\program\soffice.bin D:\Downloads\HijackThis.exe C:\Program Files (x86)\uTorrent\uTorrent.exe R1 - HKCU\Software\Microsoft\Internet Explorer\[...] [...] O2 - BHO: Java(tm) Plug-In 2 SSV Helper - {DBC80044-A445-435b-BC74-9C25C1C588A9} - C:\Program Files (x86)\Java\jre6\bin\jp2ssv.dll O4 - HKLM\..\Run: [HDAudDeck] C:\Program Files (x86)\VIA\VIAudioi\VDeck\VDeck.exe -r O4 - HKLM\..\Run: [StartCCC] "C:\Program Files (x86)\ATI Technologies\ATI.ACE\Core-Static\CLIStart.exe" MSRun O4 - HKLM\..\Run: [TurboV] "C:\Program Files\ASUS\TurboV\TurboV.exe" O4 - HKLM\..\Run: [PWRISOVM.EXE] C:\Program Files (x86)\PowerISO\PWRISOVM.EXE O4 - HKLM\..\Run: [googletalk] C:\Program Files (x86)\Google\Google Talk\googletalk.exe /autostart O4 - HKLM\..\Run: [AdobeCS4ServiceManager] "C:\Program Files (x86)\Common Files\Adobe\CS4ServiceManager\CS4ServiceManager.exe" -launchedbylogin O4 - HKCU\..\Run: [uTorrent] "C:\Program Files (x86)\uTorrent\uTorrent.exe" O4 - HKUS\S-1-5-19\..\Run: [Sidebar] %ProgramFiles%\Windows Sidebar\Sidebar.exe /autoRun (User 'LOCAL SERVICE') O4 - HKUS\S-1-5-19\..\RunOnce: [mctadmin] C:\Windows\System32\mctadmin.exe (User 'LOCAL SERVICE') O4 - HKUS\S-1-5-20\..\Run: [Sidebar] %ProgramFiles%\Windows Sidebar\Sidebar.exe /autoRun (User 'NETWORK SERVICE') O4 - HKUS\S-1-5-20\..\RunOnce: [mctadmin] C:\Windows\System32\mctadmin.exe (User 'NETWORK SERVICE') O4 - Startup: OpenOffice.org 3.1.lnk = C:\Program Files (x86)\OpenOffice.org 3\program\quickstart.exe O13 - Gopher Prefix: O23 - Service: @%SystemRoot%\system32\Alg.exe,-112 (ALG) - Unknown owner - C:\Windows\System32\alg.exe (file missing) O23 - Service: AMD External Events Utility - Unknown owner - C:\Windows\system32\atiesrxx.exe (file missing) O23 - Service: ASUS System Control Service (AsSysCtrlService) - Unknown owner - C:\Program Files (x86)\ASUS\AsSysCtrlService\1.00.02\AsSysCtrlService.exe O23 - Service: DeviceVM Meta Data Export Service (DvmMDES) - DeviceVM - C:\ASUS.SYS\config\DVMExportService.exe O23 - Service: @%SystemRoot%\system32\efssvc.dll,-100 (EFS) - Unknown owner - C:\Windows\System32\lsass.exe (file missing) O23 - Service: ESET HTTP Server (EhttpSrv) - ESET - C:\Program Files\ESET\ESET NOD32 Antivirus\EHttpSrv.exe O23 - Service: ESET Service (ekrn) - ESET - C:\Program Files\ESET\ESET NOD32 Antivirus\x86\ekrn.exe O23 - Service: @%systemroot%\system32\fxsresm.dll,-118 (Fax) - Unknown owner - C:\Windows\system32\fxssvc.exe (file missing) O23 - Service: FLEXnet Licensing Service - Acresso Software Inc. - C:\Program Files (x86)\Common Files\Macrovision Shared\FLEXnet Publisher\FNPLicensingService.exe O23 - Service: FLEXnet Licensing Service 64 - Acresso Software Inc. - C:\Program Files\Common Files\Macrovision Shared\FLEXnet Publisher\FNPLicensingService64.exe O23 - Service: InstallDriver Table Manager (IDriverT) - Macrovision Corporation - C:\Program Files (x86)\Common Files\InstallShield\Driver\11\Intel 32\IDriverT.exe O23 - Service: @keyiso.dll,-100 (KeyIso) - Unknown owner - C:\Windows\system32\lsass.exe (file missing) O23 - Service: @comres.dll,-2797 (MSDTC) - Unknown owner - C:\Windows\System32\msdtc.exe (file missing) O23 - Service: @%SystemRoot%\System32\netlogon.dll,-102 (Netlogon) - Unknown owner - C:\Windows\system32\lsass.exe (file missing) O23 - Service: @%systemroot%\system32\psbase.dll,-300 (ProtectedStorage) - Unknown owner - C:\Windows\system32\lsass.exe (file missing) O23 - Service: Protexis Licensing V2 (PSI_SVC_2) - Protexis Inc. - c:\Program Files (x86)\Common Files\Protexis\License Service\PsiService_2.exe O23 - Service: @%systemroot%\system32\Locator.exe,-2 (RpcLocator) - Unknown owner - C:\Windows\system32\locator.exe (file missing) O23 - Service: @%SystemRoot%\system32\samsrv.dll,-1 (SamSs) - Unknown owner - C:\Windows\system32\lsass.exe (file missing) O23 - Service: @%SystemRoot%\system32\snmptrap.exe,-3 (SNMPTRAP) - Unknown owner - C:\Windows\System32\snmptrap.exe (file missing) O23 - Service: @%systemroot%\system32\spoolsv.exe,-1 (Spooler) - Unknown owner - C:\Windows\System32\spoolsv.exe (file missing) O23 - Service: @%SystemRoot%\system32\sppsvc.exe,-101 (sppsvc) - Unknown owner - C:\Windows\system32\sppsvc.exe (file missing) O23 - Service: Steam Client Service - Valve Corporation - C:\Program Files (x86)\Common Files\Steam\SteamService.exe O23 - Service: @%SystemRoot%\system32\ui0detect.exe,-101 (UI0Detect) - Unknown owner - C:\Windows\system32\UI0Detect.exe (file missing) O23 - Service: @%SystemRoot%\system32\vaultsvc.dll,-1003 (VaultSvc) - Unknown owner - C:\Windows\system32\lsass.exe (file missing) O23 - Service: @%SystemRoot%\system32\vds.exe,-100 (vds) - Unknown owner - C:\Windows\System32\vds.exe (file missing) O23 - Service: @%systemroot%\system32\vssvc.exe,-102 (VSS) - Unknown owner - C:\Windows\system32\vssvc.exe (file missing) O23 - Service: @%systemroot%\system32\wbengine.exe,-104 (wbengine) - Unknown owner - C:\Windows\system32\wbengine.exe (file missing) O23 - Service: @%Systemroot%\system32\wbem\wmiapsrv.exe,-110 (wmiApSrv) - Unknown owner - C:\Windows\system32\wbem\WmiApSrv.exe (file missing) O23 - Service: @%PROGRAMFILES%\Windows Media Player\wmpnetwk.exe,-101 (WMPNetworkSvc) - Unknown owner - C:\Program Files (x86)\Windows Media Player\wmpnetwk.exe (file missing) -- End of file - 6800 bytes CPU-Z ("[...]" indicates removed text; see full log posted to pastebin): CPU-Z TXT Report ------------------------------------------------------------------------- Binaries ------------------------------------------------------------------------- CPU-Z version 1.53.1 Processors ------------------------------------------------------------------------- Number of processors 1 Number of threads 2 APICs ------------------------------------------------------------------------- Processor 0 -- Core 0 -- Thread 0 0 -- Core 1 -- Thread 0 1 Processors Information ------------------------------------------------------------------------- Processor 1 ID = 0 Number of cores 2 (max 2) Number of threads 2 (max 2) Name AMD Phenom II X2 550 Codename Callisto Specification AMD Phenom(tm) II X2 550 Processor Package Socket AM3 (938) CPUID F.4.2 Extended CPUID 10.4 Brand ID 29 Core Stepping RB-C2 Technology 45 nm Core Speed 3110.7 MHz Multiplier x FSB 15.5 x 200.7 MHz HT Link speed 2006.9 MHz Instructions sets MMX (+), 3DNow! (+), SSE, SSE2, SSE3, SSE4A, x86-64, AMD-V L1 Data cache 2 x 64 KBytes, 2-way set associative, 64-byte line size L1 Instruction cache 2 x 64 KBytes, 2-way set associative, 64-byte line size L2 cache 2 x 512 KBytes, 16-way set associative, 64-byte line size L3 cache 6 MBytes, 48-way set associative, 64-byte line size FID/VID Control yes Min FID 4.0x P-State FID 0xF - VID 0x10 P-State FID 0x8 - VID 0x18 P-State FID 0x3 - VID 0x20 P-State FID 0x100 - VID 0x2C Package Type 0x1 Model 50 String 1 0x7 String 2 0x6 Page 0x0 TDP Limit 79 Watts TDC Limit 66 Amps Attached device PCI device at bus 0, device 24, function 0 Attached device PCI device at bus 0, device 24, function 1 Attached device PCI device at bus 0, device 24, function 2 Attached device PCI device at bus 0, device 24, function 3 Attached device PCI device at bus 0, device 24, function 4 Thread dumps ------------------------------------------------------------------------- CPU Thread 0 APIC ID 0 Topology Processor ID 0, Core ID 0, Thread ID 0 Type 0200400Ah Max CPUID level 00000005h Max CPUID ext. level 8000001Bh Cache descriptor Level 1, I, 64 KB, 1 thread(s) Cache descriptor Level 1, D, 64 KB, 1 thread(s) Cache descriptor Level 2, U, 512 KB, 1 thread(s) Cache descriptor Level 3, U, 6 MB, 2 thread(s) CPUID 0x00000000 0x00000005 0x68747541 0x444D4163 0x69746E65 0x00000001 0x00100F42 0x00020800 0x00802009 0x178BFBFF 0x00000002 0x00000000 0x00000000 0x00000000 0x00000000 0x00000003 0x00000000 0x00000000 0x00000000 0x00000000 0x00000004 0x00000000 0x00000000 0x00000000 0x00000000 0x00000005 0x00000040 0x00000040 0x00000003 0x00000000 [...] CPU Thread 1 APIC ID 1 Topology Processor ID 0, Core ID 1, Thread ID 0 Type 0200400Ah Max CPUID level 00000005h Max CPUID ext. level 8000001Bh Cache descriptor Level 1, I, 64 KB, 1 thread(s) Cache descriptor Level 1, D, 64 KB, 1 thread(s) Cache descriptor Level 2, U, 512 KB, 1 thread(s) Cache descriptor Level 3, U, 6 MB, 2 thread(s) CPUID 0x00000000 0x00000005 0x68747541 0x444D4163 0x69746E65 0x00000001 0x00100F42 0x01020800 0x00802009 0x178BFBFF 0x00000002 0x00000000 0x00000000 0x00000000 0x00000000 0x00000003 0x00000000 0x00000000 0x00000000 0x00000000 0x00000004 0x00000000 0x00000000 0x00000000 0x00000000 0x00000005 0x00000040 0x00000040 0x00000003 0x00000000 [...] Chipset ------------------------------------------------------------------------- Northbridge AMD 790GX rev. 00 Southbridge ATI SB750 rev. 00 Memory Type DDR3 Memory Size 4096 MBytes Channels Dual, (Unganged) Memory Frequency 669.0 MHz (3:10) CAS# latency (CL) 9.0 RAS# to CAS# delay (tRCD) 9 RAS# Precharge (tRP) 9 Cycle Time (tRAS) 24 Bank Cycle Time (tRC) 33 Command Rate (CR) 1T Uncore Frequency 2006.9 MHz Memory SPD ------------------------------------------------------------------------- DIMM # 1 SMBus address 0x50 Memory type DDR3 Module format UDIMM Manufacturer (ID) G.Skill (7F7F7F7FCD000000) Size 2048 MBytes Max bandwidth PC3-10700 (667 MHz) Part number F3-10600CL9-2GBNT Number of banks 8 Nominal Voltage 1.50 Volts EPP no XMP no JEDEC timings table CL-tRCD-tRP-tRAS-tRC @ frequency JEDEC #1 6.0-6-6-17-23 @ 457 MHz JEDEC #2 7.0-7-7-20-27 @ 533 MHz JEDEC #3 8.0-8-8-22-31 @ 609 MHz JEDEC #4 9.0-9-9-25-34 @ 685 MHz DIMM # 2 SMBus address 0x51 Memory type DDR3 Module format UDIMM Manufacturer (ID) G.Skill (7F7F7F7FCD000000) Size 2048 MBytes Max bandwidth PC3-10700 (667 MHz) Part number F3-10600CL9-2GBNT Number of banks 8 Nominal Voltage 1.50 Volts EPP no XMP no JEDEC timings table CL-tRCD-tRP-tRAS-tRC @ frequency JEDEC #1 6.0-6-6-17-23 @ 457 MHz JEDEC #2 7.0-7-7-20-27 @ 533 MHz JEDEC #3 8.0-8-8-22-31 @ 609 MHz JEDEC #4 9.0-9-9-25-34 @ 685 MHz DIMM # 1 SPD registers [...] DIMM # 2 SPD registers [...] Monitoring ------------------------------------------------------------------------- Mainboard Model M4A78T-E (0x000001F7 - 0x00A955E4) LPCIO ------------------------------------------------------------------------- LPCIO Vendor ITE LPCIO Model IT8720 LPCIO Vendor ID 0x90 LPCIO Chip ID 0x8720 LPCIO Revision ID 0x2 Config Mode I/O address 0x2E Config Mode LDN 0x4 Config Mode registers [...] Register space LPC, base address = 0x0290 Hardware Monitors ------------------------------------------------------------------------- Hardware monitor ITE IT87 Voltage 1 1.62 Volts [0x65] (VIN1) Voltage 2 1.15 Volts [0x48] (CPU VCORE) Voltage 3 5.03 Volts [0xBB] (+5V) Voltage 8 3.34 Volts [0xD1] (VBAT) Temperature 0 39°C (102°F) [0x27] (TMPIN0) Temperature 1 43°C (109°F) [0x2B] (TMPIN1) Fan 0 3096 RPM [0xDA] (FANIN0) Register space LPC, base address = 0x0290 [...] Hardware monitor AMD SB6xx/7xx Voltage 0 1.37 Volts [0x1D2] (CPU VCore) Voltage 1 3.50 Volts [0x27B] (CPU IO) Voltage 2 12.68 Volts [0x282] (+12V) Hardware monitor AMD Phenom II X2 550 Power 0 89.10 W (Processor) Temperature 0 35°C (94°F) [0x115] (Core #0) Temperature 1 35°C (94°F) [0x115] (Core #1)

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KVM Slow performance on XP Guest

- by Gregg Leventhal

The system is very slow to do anything, even browse a local folder, and CPU sits at 100% frequently. Guest is XP 32 bit. Host is Scientific Linux 6.2, Libvirt 0.10, Guest XP OS shows ACPI Multiprocessor HAL and a virtIO driver for NIC and SCSI. Installed. CPUInfo on host: processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz stepping : 7 cpu MHz : 3200.000 cache size : 8192 KB physical id : 0 siblings : 8 core id : 0 cpu cores : 4 apicid : 0 initial apicid : 0 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good xtopology nonstop_tsc aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx lahf_lm ida arat epb xsaveopt pln pts dts tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.93 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: <memory unit='KiB'>4194304</memory> <currentMemory unit='KiB'>4194304</currentMemory> <vcpu placement='static' cpuset='0'>1</vcpu> <os> <type arch='x86_64' machine='rhel6.3.0'>hvm</type> <boot dev='hd'/> </os> <features> <acpi/> <apic/> <pae/> </features> <cpu mode='custom' match='exact'> <model fallback='allow'>SandyBridge</model> <vendor>Intel</vendor> <feature policy='require' name='vme'/> <feature policy='require' name='tm2'/> <feature policy='require' name='est'/> <feature policy='require' name='vmx'/> <feature policy='require' name='osxsave'/> <feature policy='require' name='smx'/> <feature policy='require' name='ss'/> <feature policy='require' name='ds'/> <feature policy='require' name='tsc-deadline'/> <feature policy='require' name='dtes64'/> <feature policy='require' name='ht'/> <feature policy='require' name='pbe'/> <feature policy='require' name='tm'/> <feature policy='require' name='pdcm'/> <feature policy='require' name='ds_cpl'/> <feature policy='require' name='xtpr'/> <feature policy='require' name='acpi'/> <feature policy='require' name='monitor'/> <feature policy='force' name='sse'/> <feature policy='force' name='sse2'/> <feature policy='force' name='sse4.1'/> <feature policy='force' name='sse4.2'/> <feature policy='force' name='ssse3'/> <feature policy='force' name='x2apic'/> </cpu> <clock offset='localtime'> <timer name='rtc' tickpolicy='catchup'/> </clock> <on_poweroff>destroy</on_poweroff> <on_reboot>restart</on_reboot> <on_crash>restart</on_crash> <devices> <emulator>/usr/libexec/qemu-kvm</emulator> <disk type='file' device='disk'> <driver name='qemu' type='qcow2' cache='none'/> <source file='/var/lib/libvirt/images/Server-10-9-13.qcow2'/> <target dev='vda' bus='virtio'/> <alias name='virtio-disk0'/> <address type='pci' domain='0x0000' bus='0x00' slot='0x08' function='0x0'/> </disk>

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nginx+php-fpm help optimize configs

- by Dmitro

I have 3 servers. First server (CPU - model name: 06/17, 2.66GHz, 4 cores, 8GB RAM) have nginx as load balancer with next config upstream lb_mydomain { server mydomain.ru:81 weight=2; server 66.0.0.18 weight=6; } server { listen 80; server_name ~(?!mydomain.ru)(.*); client_max_body_size 20m; location / { proxy_pass http://lb_mydomain; proxy_redirect off; proxy_set_header Connection close; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_pass_header Set-Cookie; proxy_pass_header P3P; proxy_pass_header Content-Type; proxy_pass_header Content-Disposition; proxy_pass_header Content-Length; } } And configs from nginx.conf: user www-data; worker_processes 5; # worker_priority -1; error_log /var/log/nginx/error.log; pid /var/run/nginx.pid; events { worker_connections 5024; # multi_accept on; } http { include /etc/nginx/mime.types; access_log /var/log/nginx/access.log; sendfile on; default_type application/octet-stream; #tcp_nopush on; keepalive_timeout 65; tcp_nodelay on; gzip on; gzip_disable "MSIE [1-6]\.(?!.*SV1)"; # PHP-FPM (backend) upstream php-fpm { server 127.0.0.1:9000; } include /etc/nginx/conf.d/*.conf; include /etc/nginx/sites-enabled/*; } And config php-fpm: listen = 127.0.0.1:9000 ;listen.backlog = -1 ;listen.allowed_clients = 127.0.0.1 ;listen.owner = www-data ;listen.group = www-data ;listen.mode = 0666 user = www-data group = www-data pm = dynamic pm.max_children = 80 ;pm.start_servers = 20 pm.min_spare_servers = 5 pm.max_spare_servers = 35 ;pm.max_requests = 500 pm.status_path = /status ping.path = /ping ;ping.response = pong request_terminate_timeout = 30s request_slowlog_timeout = 10s slowlog = /var/log/php-fpm.log.slow ;rlimit_files = 1024 ;rlimit_core = 0 ;chroot = chdir = /var/www ;catch_workers_output = yes ;env[HOSTNAME] = $HOSTNAME ;env[PATH] = /usr/local/bin:/usr/bin:/bin ;env[TMP] = /tmp ;env[TMPDIR] = /tmp ;env[TEMP] = /tmp ;php_admin_value[sendmail_path] = /usr/sbin/sendmail -t -i -f [email protected] ;php_flag[display_errors] = off ;php_admin_value[error_log] = /var/log/fpm-php.www.log ;php_admin_flag[log_errors] = on ;php_admin_value[memory_limit] = 32M In top I see 20 php-fpm processes which use from 1% - 15% CPU. So it's have high load averadge: top - 15:36:22 up 34 days, 20:54, 1 user, load average: 5.98, 7.75, 8.78 Tasks: 218 total, 1 running, 217 sleeping, 0 stopped, 0 zombie Cpu(s): 34.1%us, 3.2%sy, 0.0%ni, 37.0%id, 24.8%wa, 0.0%hi, 0.9%si, 0.0%st Mem: 8183228k total, 7538584k used, 644644k free, 351136k buffers Swap: 9936892k total, 14636k used, 9922256k free, 990540k cached Second server(CPU - model name: Intel(R) Xeon(R) CPU E5504 @ 2.00GHz, 8 cores, 8GB RAM). Nginx configs from nginx.conf: user www-data; worker_processes 5; # worker_priority -1; error_log /var/log/nginx/error.log; pid /var/run/nginx.pid; events { worker_connections 5024; # multi_accept on; } http { include /etc/nginx/mime.types; access_log /var/log/nginx/access.log; sendfile on; default_type application/octet-stream; #tcp_nopush on; keepalive_timeout 65; tcp_nodelay on; gzip on; gzip_disable "MSIE [1-6]\.(?!.*SV1)"; # PHP-FPM (backend) upstream php-fpm { server 127.0.0.1:9000; } include /etc/nginx/conf.d/*.conf; include /etc/nginx/sites-enabled/*; } And config of php-fpm: listen = 127.0.0.1:9000 ;listen.backlog = -1 ;listen.allowed_clients = 127.0.0.1 ;listen.owner = www-data ;listen.group = www-data ;listen.mode = 0666 user = www-data group = www-data pm = dynamic pm.max_children = 50 ;pm.start_servers = 20 pm.min_spare_servers = 5 pm.max_spare_servers = 35 ;pm.max_requests = 500 ;pm.status_path = /status ;ping.path = /ping ;ping.response = pong ;request_terminate_timeout = 0 ;request_slowlog_timeout = 0 ;slowlog = /var/log/php-fpm.log.slow ;rlimit_files = 1024 ;rlimit_core = 0 ;chroot = chdir = /var/www ;catch_workers_output = yes ;env[HOSTNAME] = $HOSTNAME ;env[PATH] = /usr/local/bin:/usr/bin:/bin ;env[TMP] = /tmp ;env[TMPDIR] = /tmp ;env[TEMP] = /tmp ;php_admin_value[sendmail_path] = /usr/sbin/sendmail -t -i -f [email protected] ;php_flag[display_errors] = off ;php_admin_value[error_log] = /var/log/fpm-php.www.log ;php_admin_flag[log_errors] = on ;php_admin_value[memory_limit] = 32M In top I see 50 php-fpm processes which use from 10% - 25% CPU. So it's have high load averadge: top - 15:53:05 up 33 days, 1:15, 1 user, load average: 41.35, 40.28, 39.61 Tasks: 239 total, 40 running, 199 sleeping, 0 stopped, 0 zombie Cpu(s): 96.5%us, 3.1%sy, 0.0%ni, 0.0%id, 0.0%wa, 0.0%hi, 0.4%si, 0.0%st Mem: 8185560k total, 7804224k used, 381336k free, 161648k buffers Swap: 19802108k total, 16k used, 19802092k free, 5068112k cached Third server is server with database postgresql. Also i try ab -n 50 -c 5 http://www.mydomain.ru/ And I get next info: Complete requests: 50 Failed requests: 48 (Connect: 0, Receive: 0, Length: 48, Exceptions: 0) Write errors: 0 Total transferred: 9271367 bytes HTML transferred: 9247767 bytes Requests per second: 1.02 [#/sec] (mean) Time per request: 4882.427 [ms] (mean) Time per request: 976.486 [ms] (mean, across all concurrent requests) Transfer rate: 185.44 [Kbytes/sec] received Please advise how can I make lower level of load average?

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Java: Netbeans debugging session works faster than normal run

- by Martijn Courteaux

Hello, I'm making Braid in Netbeans 6.7.1. Computer Spec: Windows 7 Running processes: 46 Running threads: +/- 650 NVidia GeForce 9200M GS Intel Core 2 Duo CPU P8400 @ 2.26Ghz Game-spec with normal run: Memory: between 80 MB and 110 MB CPU: between 9% and 20% CPU when time rewinding: 90% The same values for the debugging session, except when I rewind the time: CPU: 20%. Is there any reason for? Is there a way to reach the same performance with a normal run. This is my repaint code: @Override public void repaint() { BufferStrategy bs = getBufferStrategy(); // numBuffers: 4 Graphics g = bs.getDrawGraphics(); g.setColor(Color.BLACK); g.fillRect(-1, -1, 2000, 2000); gamePanel.paint(g.create(x, y, gameDim.width, gameDim.height)); bs.show(); g.dispose(); Toolkit.getDefaultToolkit().sync(); update(g); } The game runs in fullscreen (undecorated + frame.size = screensize) Martijn

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Bad temperature sensors on Foxconn motherboard?

- by Gawain

I have a system with a Foxconn V400 series motherboard and AMD Athlon 3000+ processor. Ever since I got it a few years ago the fans (particularly the CPU fan) have been really loud. So recently I installed SpeedFan to see why they were running so fast. SpeedFan reported the CPU temperature to be 32C, and one motherboard sensor at about 26C. But the other two motherboard sensors were reporting 78C and 64C respectively. Naturally the fans were both maxed out because of this, with the CPU fan at 5800rpm and the case fan at 2400rpm. I opened the case and everything inside was literally cool to the touch, with the exception of the CPU heatsink which was slightly warm, but nowhere near 78C. It seems like the temperature sensors are either defective or being read incorrectly. Is there some way I can decrease my fan noise without risking damage to my processor? Some way to ignore those two temp sensors? Any help would be greatly appreciated.

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My "PHP-quote" of the day :)

- by lost3den

"Because of a higher CPU Usage, PHP isn't echo friendly" Hope you like it :)

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Generate a list of file names based on month and year arithmetic

- by MacUsers

How can I list the numbers 01 to 12 (one for each of the 12 months) in such a way so that the current month always comes last where the oldest one is first. In other words, if the number is grater than the current month, it's from the previous year. e.g. 02 is Feb, 2011 (the current month right now), 03 is March, 2010 and 09 is Sep, 2010 but 01 is Jan, 2011. In this case, I'd like to have [09, 03, 01, 02]. This is what I'm doing to determine the year: for inFile in os.listdir('.'): if inFile.isdigit(): month = months[int(inFile)] if int(inFile) <= int(strftime("%m")): year = strftime("%Y") else: year = int(strftime("%Y"))-1 mnYear = month + ", " + str(year) I don't have a clue what to do next. What should I do here? Update: I think, I better upload the entire script for better understanding. #!/usr/bin/env python import os, sys from time import strftime from calendar import month_abbr vGroup = {} vo = "group_lhcb" SI00_fig = float(2.478) months = tuple(month_abbr) print "\n%-12s\t%10s\t%8s\t%10s" % ('VOs','CPU-time','CPU-time','kSI2K-hrs') print "%-12s\t%10s\t%8s\t%10s" % ('','(in Sec)','(in Hrs)','(*2.478)') print "=" * 58 for inFile in os.listdir('.'): if inFile.isdigit(): readFile = open(inFile, 'r') lines = readFile.readlines() readFile.close() month = months[int(inFile)] if int(inFile) <= int(strftime("%m")): year = strftime("%Y") else: year = int(strftime("%Y"))-1 mnYear = month + ", " + str(year) for line in lines[2:]: if line.find(vo)==0: g, i = line.split() s = vGroup.get(g, 0) vGroup[g] = s + int(i) sumHrs = ((vGroup[g]/60)/60) sumSi2k = sumHrs*SI00_fig print "%-12s\t%10s\t%8s\t%10.2f" % (mnYear,vGroup[g],sumHrs,sumSi2k) del vGroup[g] When I run the script, I get this: [root@serv07 usage]# ./test.py VOs CPU-time CPU-time kSI2K-hrs (in Sec) (in Hrs) (*2.478) ================================================== Jan, 2011 211201372 58667 145376.83 Dec, 2010 5064337 1406 3484.07 Feb, 2011 17506049 4862 12048.04 Sep, 2010 210874275 58576 145151.33 As I said in the original post, I like the result to be in this order instead: Sep, 2010 210874275 58576 145151.33 Dec, 2010 5064337 1406 3484.07 Jan, 2011 211201372 58667 145376.83 Feb, 2011 17506049 4862 12048.04 The files in the source directory reads like this: [root@serv07 usage]# ls -l total 3632 -rw-r--r-- 1 root root 1144972 Feb 9 19:23 01 -rw-r--r-- 1 root root 556630 Feb 13 09:11 02 -rw-r--r-- 1 root root 443782 Feb 11 17:23 02.bak -rw-r--r-- 1 root root 1144556 Feb 14 09:30 09 -rw-r--r-- 1 root root 370822 Feb 9 19:24 12 Did I give a better picture now? Sorry for not being very clear in the first place. Cheers!! Update @Mark Ransom This is the result from Mark's suggestion: [root@serv07 usage]# ./test.py VOs CPU-time CPU-time kSI2K-hrs (in Sec) (in Hrs) (*2.478) ========================================================== Dec, 2010 5064337 1406 3484.07 Sep, 2010 210874275 58576 145151.33 Feb, 2011 17506049 4862 12048.04 Jan, 2011 211201372 58667 145376.83 As I said before, I'm looking for the result to b printed in this order: Sep, 2010 - Dec, 2010 - Jan, 2011 - Feb, 2011 Cheers!!

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Execute a command using php under ssh2 in php

- by Mervyn

Using Mint terminal my script connects using ssh2_connect and ssh2_auth-password. When am logged in successfully I want to run a command which will give me the hardware cpu. Is there a way I can use to exec the command in my script then show the results. I have used system and exec for pinging. if i was in the terminal i do the login. then type "get hardware cpu" in the terminal it would look like this: Test~ $ get hardware cpu

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Java: "cannot find symbol" error of a String[] defined within a while-loop

- by David

Here's the relevant code: public static String[] runTeams (String CPUcolor) { boolean z = false ; //String[] a = new String[6] ; boolean CPU = false ; while (z == false) { while (CPU==false) { String[] a = assignTeams () ; printOrder (a) ; for (int i = 1; i<a.length; i++) { if (a[i].equals(CPUcolor)) CPU = true ; } if (CPU==false) { System.out.println ("ERROR YOU NEED TO INCLUDE THE COLOR OF THE CPU IN THE TURN ORDER") ; } } System.out.println ("is this turn order correct? (Y/N)") ; String s = getIns () ; while (!((s.equals ("y")) || (s.equals ("Y")) || (s.equals ("n")) || (s.equals ("N")))) { System.out.println ("try again") ; s = getIns () ; } if (s.equals ("y") || s.equals ("Y") ) z = true ; } return a ; } the error i get is: Risk.java:416: cannot find symbol symbol : variable a location: class Risk return a ; ^ Why did i get this error? It seems that a is clearly defined in the line String[] a = assignTeams () ; and if anything is used by the lineprintOrder (a) ;` it seems to me that if the symbol a really couldn't be found then the compiler should blow up there and not at the return statment. (also the method assignTeams returns an array of Strings.)

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Unity not Working 14.04

- by Back.Slash

I am using Ubuntu 14.04 LTS x64. I did a sudo apt-get upgrade yesterday and restarted my PC. Now my taskbar and panel are missing. When I try to restart Unity using unity --replace Then I get error: unity-panel-service stop/waiting compiz (core) - Info: Loading plugin: core compiz (core) - Info: Starting plugin: core unity-panel-service start/running, process 3906 compiz (core) - Info: Loading plugin: ccp compiz (core) - Info: Starting plugin: ccp compizconfig - Info: Backend : gsettings compizconfig - Info: Integration : true compizconfig - Info: Profile : unity compiz (core) - Info: Loading plugin: composite compiz (core) - Info: Starting plugin: composite compiz (core) - Info: Loading plugin: opengl compiz (core) - Info: Unity is fully supported by your hardware. compiz (core) - Info: Unity is fully supported by your hardware. compiz (core) - Info: Starting plugin: opengl libGL error: dlopen /usr/lib/x86_64-linux-gnu/dri/i965_dri.so failed (/usr/lib/x86_64-linux-gnu/dri/i965_dri.so: undefined symbol: _glapi_tls_Dispatch) libGL error: dlopen ${ORIGIN}/dri/i965_dri.so failed (${ORIGIN}/dri/i965_dri.so: cannot open shared object file: No such file or directory) libGL error: dlopen /usr/lib/dri/i965_dri.so failed (/usr/lib/dri/i965_dri.so: cannot open shared object file: No such file or directory) libGL error: unable to load driver: i965_dri.so libGL error: driver pointer missing libGL error: failed to load driver: i965 libGL error: dlopen /usr/lib/x86_64-linux-gnu/dri/swrast_dri.so failed (/usr/lib/x86_64-linux-gnu/dri/swrast_dri.so: undefined symbol: _glapi_tls_Dispatch) libGL error: dlopen ${ORIGIN}/dri/swrast_dri.so failed (${ORIGIN}/dri/swrast_dri.so: cannot open shared object file: No such file or directory) libGL error: dlopen /usr/lib/dri/swrast_dri.so failed (/usr/lib/dri/swrast_dri.so: cannot open shared object file: No such file or directory) libGL error: unable to load driver: swrast_dri.so libGL error: failed to load driver: swrast compiz (core) - Info: Loading plugin: compiztoolbox compiz (core) - Info: Starting plugin: compiztoolbox compiz (core) - Info: Loading plugin: decor compiz (core) - Info: Starting plugin: decor compiz (core) - Info: Loading plugin: vpswitch compiz (core) - Info: Starting plugin: vpswitch compiz (core) - Info: Loading plugin: snap compiz (core) - Info: Starting plugin: snap compiz (core) - Info: Loading plugin: mousepoll compiz (core) - Info: Starting plugin: mousepoll compiz (core) - Info: Loading plugin: resize compiz (core) - Info: Starting plugin: resize compiz (core) - Info: Loading plugin: place compiz (core) - Info: Starting plugin: place compiz (core) - Info: Loading plugin: move compiz (core) - Info: Starting plugin: move compiz (core) - Info: Loading plugin: wall compiz (core) - Info: Starting plugin: wall compiz (core) - Info: Loading plugin: grid compiz (core) - Info: Starting plugin: grid compiz (core) - Info: Loading plugin: regex compiz (core) - Info: Starting plugin: regex compiz (core) - Info: Loading plugin: imgpng compiz (core) - Info: Starting plugin: imgpng compiz (core) - Info: Loading plugin: session compiz (core) - Info: Starting plugin: session I/O warning : failed to load external entity "/home/sumeet/.compiz/session/10de541a813cc1a8fc140170575114755000000020350005" compiz (core) - Info: Loading plugin: gnomecompat compiz (core) - Info: Starting plugin: gnomecompat compiz (core) - Info: Loading plugin: animation compiz (core) - Info: Starting plugin: animation compiz (core) - Info: Loading plugin: fade compiz (core) - Info: Starting plugin: fade compiz (core) - Info: Loading plugin: unitymtgrabhandles compiz (core) - Info: Starting plugin: unitymtgrabhandles compiz (core) - Info: Loading plugin: workarounds compiz (core) - Info: Starting plugin: workarounds compiz (core) - Info: Loading plugin: scale compiz (core) - Info: Starting plugin: scale compiz (core) - Info: Loading plugin: expo compiz (core) - Info: Starting plugin: expo compiz (core) - Info: Loading plugin: ezoom compiz (core) - Info: Starting plugin: ezoom compiz (core) - Info: Loading plugin: unityshell compiz (core) - Info: Starting plugin: unityshell WARN 2014-06-02 18:46:23 unity.glib.dbus.server GLibDBusServer.cpp:579 Can't register object 'org.gnome.Shell' yet as we don't have a connection, waiting for it... ERROR 2014-06-02 18:46:23 unity.debug.interface DebugDBusInterface.cpp:216 Unable to load entry point in libxpathselect: libxpathselect.so.1.4: cannot open shared object file: No such file or directory compiz (unityshell) - Error: GL_ARB_vertex_buffer_object not supported ERROR 2014-06-02 18:46:23 unity.shell.compiz unityshell.cpp:3850 Impossible to delete the unity locked stamp file compiz (core) - Error: Plugin initScreen failed: unityshell compiz (core) - Error: Failed to start plugin: unityshell compiz (core) - Info: Unloading plugin: unityshell X Error of failed request: BadWindow (invalid Window parameter) Major opcode of failed request: 3 (X_GetWindowAttributes) Resource id in failed request: 0x3e000c9 Serial number of failed request: 10115 Current serial number in output stream: 10116 Any help would be highly appreciated. EDIT : My PC configuration description: Portable Computer product: Dell System XPS L502X (System SKUNumber) vendor: Dell Inc. version: 0.1 serial: 1006ZP1 width: 64 bits capabilities: smbios-2.6 dmi-2.6 vsyscall32 configuration: administrator_password=unknown boot=normal chassis=portable family=HuronRiver System frontpanel_password=unknown keyboard_password=unknown power-on_password=unknown sku=System SKUNumber uuid=44454C4C-3000-1030-8036-B1C04F5A5031 *-core description: Motherboard product: 0YR8NN vendor: Dell Inc. physical id: 0 version: A00 serial: .1006ZP1.CN4864314C0560. slot: Part Component *-firmware description: BIOS vendor: Dell Inc. physical id: 0 version: A11 date: 05/29/2012 size: 128KiB capacity: 2496KiB capabilities: pci pnp upgrade shadowing escd cdboot bootselect socketedrom edd int13floppy360 int13floppy1200 int13floppy720 int5printscreen int9keyboard int14serial int17printer int10video acpi usb ls120boot smartbattery biosbootspecification netboot *-cpu description: CPU product: Intel(R) Core(TM) i7-2630QM CPU @ 2.00GHz vendor: Intel Corp. physical id: 19 bus info: cpu@0 version: Intel(R) Core(TM) i7-2630QM CPU @ 2.00GHz serial: Not Supported by CPU slot: CPU size: 800MHz capacity: 800MHz width: 64 bits clock: 100MHz capabilities: x86-64 fpu fpu_exception wp vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp constant_tsc arch_perfmon pebs bts nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx lahf_lm ida arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid cpufreq configuration: cores=4 enabledcores=4 threads=8 *-cache:0 description: L1 cache physical id: 1a slot: L1-Cache size: 64KiB capacity: 64KiB capabilities: synchronous internal write-through data *-cache:1 description: L2 cache physical id: 1b slot: L2-Cache size: 256KiB capacity: 256KiB capabilities: synchronous internal write-through data *-cache:2 description: L3 cache physical id: 1c slot: L3-Cache size: 6MiB capacity: 6MiB capabilities: synchronous internal write-back unified *-memory description: System Memory physical id: 1d slot: System board or motherboard size: 6GiB *-bank:0 description: SODIMM DDR3 Synchronous 1333 MHz (0.8 ns) product: M471B5273DH0-CH9 vendor: Samsung physical id: 0 serial: 450F1160 slot: ChannelA-DIMM0 size: 4GiB width: 64 bits clock: 1333MHz (0.8ns) *-bank:1 description: SODIMM DDR3 Synchronous 1333 MHz (0.8 ns) product: HMT325S6BFR8C-H9 vendor: Hynix/Hyundai physical id: 1 serial: 0CA0E8E2 slot: ChannelB-DIMM0 size: 2GiB width: 64 bits clock: 1333MHz (0.8ns) *-pci description: Host bridge product: 2nd Generation Core Processor Family DRAM Controller vendor: Intel Corporation physical id: 100 bus info: pci@0000:00:00.0 version: 09 width: 32 bits clock: 33MHz *-pci:0 description: PCI bridge product: Xeon E3-1200/2nd Generation Core Processor Family PCI Express Root Port vendor: Intel Corporation physical id: 1 bus info: pci@0000:00:01.0 version: 09 width: 32 bits clock: 33MHz capabilities: pci pm msi pciexpress normal_decode bus_master cap_list configuration: driver=pcieport resources: irq:40 ioport:3000(size=4096) memory:f0000000-f10fffff ioport:c0000000(size=301989888) *-generic UNCLAIMED description: Unassigned class product: Illegal Vendor ID vendor: Illegal Vendor ID physical id: 0 bus info: pci@0000:01:00.0 version: ff width: 32 bits clock: 66MHz capabilities: bus_master vga_palette cap_list configuration: latency=255 maxlatency=255 mingnt=255 resources: memory:f0000000-f0ffffff memory:c0000000-cfffffff memory:d0000000-d1ffffff ioport:3000(size=128) memory:f1000000-f107ffff *-display description: VGA compatible controller product: 2nd Generation Core Processor Family Integrated Graphics Controller vendor: Intel Corporation physical id: 2 bus info: pci@0000:00:02.0 version: 09 width: 64 bits clock: 33MHz capabilities: msi pm vga_controller bus_master cap_list rom configuration: driver=i915 latency=0 resources: irq:52 memory:f1400000-f17fffff memory:e0000000-efffffff ioport:4000(size=64) *-communication description: Communication controller product: 6 Series/C200 Series Chipset Family MEI Controller #1 vendor: Intel Corporation physical id: 16 bus info: pci@0000:00:16.0 version: 04 width: 64 bits clock: 33MHz capabilities: pm msi bus_master cap_list configuration: driver=mei_me latency=0 resources: irq:50 memory:f1c05000-f1c0500f *-usb:0 description: USB controller product: 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #2 vendor: Intel Corporation physical id: 1a bus info: pci@0000:00:1a.0 version: 05 width: 32 bits clock: 33MHz capabilities: pm debug ehci bus_master cap_list configuration: driver=ehci-pci latency=0 resources: irq:16 memory:f1c09000-f1c093ff *-multimedia description: Audio device product: 6 Series/C200 Series Chipset Family High Definition Audio Controller vendor: Intel Corporation physical id: 1b bus info: pci@0000:00:1b.0 version: 05 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list configuration: driver=snd_hda_intel latency=0 resources: irq:53 memory:f1c00000-f1c03fff *-pci:1 description: PCI bridge product: 6 Series/C200 Series Chipset Family PCI Express Root Port 1 vendor: Intel Corporation physical id: 1c bus info: pci@0000:00:1c.0 version: b5 width: 32 bits clock: 33MHz capabilities: pci pciexpress msi pm normal_decode cap_list configuration: driver=pcieport resources: irq:16 *-pci:2 description: PCI bridge product: 6 Series/C200 Series Chipset Family PCI Express Root Port 2 vendor: Intel Corporation physical id: 1c.1 bus info: pci@0000:00:1c.1 version: b5 width: 32 bits clock: 33MHz capabilities: pci pciexpress msi pm normal_decode bus_master cap_list configuration: driver=pcieport resources: irq:17 memory:f1b00000-f1bfffff *-network description: Wireless interface product: Centrino Wireless-N 1030 [Rainbow Peak] vendor: Intel Corporation physical id: 0 bus info: pci@0000:03:00.0 logical name: mon.wlan0 version: 34 serial: bc:77:37:14:47:e5 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list logical wireless ethernet physical configuration: broadcast=yes driver=iwlwifi driverversion=3.13.0-27-generic firmware=18.168.6.1 latency=0 link=no multicast=yes wireless=IEEE 802.11bgn resources: irq:51 memory:f1b00000-f1b01fff *-pci:3 description: PCI bridge product: 6 Series/C200 Series Chipset Family PCI Express Root Port 4 vendor: Intel Corporation physical id: 1c.3 bus info: pci@0000:00:1c.3 version: b5 width: 32 bits clock: 33MHz capabilities: pci pciexpress msi pm normal_decode bus_master cap_list configuration: driver=pcieport resources: irq:19 memory:f1a00000-f1afffff *-usb description: USB controller product: uPD720200 USB 3.0 Host Controller vendor: NEC Corporation physical id: 0 bus info: pci@0000:04:00.0 version: 04 width: 64 bits clock: 33MHz capabilities: pm msi msix pciexpress xhci bus_master cap_list configuration: driver=xhci_hcd latency=0 resources: irq:19 memory:f1a00000-f1a01fff *-pci:4 description: PCI bridge product: 6 Series/C200 Series Chipset Family PCI Express Root Port 5 vendor: Intel Corporation physical id: 1c.4 bus info: pci@0000:00:1c.4 version: b5 width: 32 bits clock: 33MHz capabilities: pci pciexpress msi pm normal_decode bus_master cap_list configuration: driver=pcieport resources: irq:16 memory:f1900000-f19fffff *-pci:5 description: PCI bridge product: 6 Series/C200 Series Chipset Family PCI Express Root Port 6 vendor: Intel Corporation physical id: 1c.5 bus info: pci@0000:00:1c.5 version: b5 width: 32 bits clock: 33MHz capabilities: pci pciexpress msi pm normal_decode bus_master cap_list configuration: driver=pcieport resources: irq:17 ioport:2000(size=4096) ioport:f1800000(size=1048576) *-network description: Ethernet interface product: RTL8111/8168/8411 PCI Express Gigabit Ethernet Controller vendor: Realtek Semiconductor Co., Ltd. physical id: 0 bus info: pci@0000:06:00.0 logical name: eth0 version: 06 serial: 14:fe:b5:a3:ac:40 size: 1Gbit/s capacity: 1Gbit/s width: 64 bits clock: 33MHz capabilities: pm msi pciexpress msix vpd bus_master cap_list ethernet physical tp mii 10bt 10bt-fd 100bt 100bt-fd 1000bt 1000bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=r8169 driverversion=2.3LK-NAPI duplex=full firmware=rtl_nic/rtl8168e-2.fw ip=172.19.167.151 latency=0 link=yes multicast=yes port=MII speed=1Gbit/s resources: irq:49 ioport:2000(size=256) memory:f1804000-f1804fff memory:f1800000-f1803fff *-usb:1 description: USB controller product: 6 Series/C200 Series Chipset Family USB Enhanced Host Controller #1 vendor: Intel Corporation physical id: 1d bus info: pci@0000:00:1d.0 version: 05 width: 32 bits clock: 33MHz capabilities: pm debug ehci bus_master cap_list configuration: driver=ehci-pci latency=0 resources: irq:23 memory:f1c08000-f1c083ff *-isa description: ISA bridge product: HM67 Express Chipset Family LPC Controller vendor: Intel Corporation physical id: 1f bus info: pci@0000:00:1f.0 version: 05 width: 32 bits clock: 33MHz capabilities: isa bus_master cap_list configuration: driver=lpc_ich latency=0 resources: irq:0 *-ide:0 description: IDE interface product: 6 Series/C200 Series Chipset Family 4 port SATA IDE Controller vendor: Intel Corporation physical id: 1f.2 bus info: pci@0000:00:1f.2 version: 05 width: 32 bits clock: 66MHz capabilities: ide pm bus_master cap_list configuration: driver=ata_piix latency=0 resources: irq:19 ioport:40b8(size=8) ioport:40cc(size=4) ioport:40b0(size=8) ioport:40c8(size=4) ioport:4090(size=16) ioport:4080(size=16) *-serial UNCLAIMED description: SMBus product: 6 Series/C200 Series Chipset Family SMBus Controller vendor: Intel Corporation physical id: 1f.3 bus info: pci@0000:00:1f.3 version: 05 width: 64 bits clock: 33MHz configuration: latency=0 resources: memory:f1c04000-f1c040ff ioport:efa0(size=32) *-ide:1 description: IDE interface product: 6 Series/C200 Series Chipset Family 2 port SATA IDE Controller vendor: Intel Corporation physical id: 1f.5 bus info: pci@0000:00:1f.5 version: 05 width: 32 bits clock: 66MHz capabilities: ide pm bus_master cap_list configuration: driver=ata_piix latency=0 resources: irq:19 ioport:40a8(size=8) ioport:40c4(size=4) ioport:40a0(size=8) ioport:40c0(size=4) ioport:4070(size=16) ioport:4060(size=16) *-scsi:0 physical id: 1 logical name: scsi0 capabilities: emulated *-disk description: ATA Disk product: SAMSUNG HN-M640M physical id: 0.0.0 bus info: scsi@0:0.0.0 logical name: /dev/sda version: 2AR1 serial: S2T3J1KBC00006 size: 596GiB (640GB) capabilities: partitioned partitioned:dos configuration: ansiversion=5 sectorsize=512 signature=6b746d91 *-volume:0 description: Windows NTFS volume physical id: 1 bus info: scsi@0:0.0.0,1 logical name: /dev/sda1 version: 3.1 serial: 0272-3e7f size: 348MiB capacity: 350MiB capabilities: primary bootable ntfs initialized configuration: clustersize=4096 created=2013-09-18 12:20:45 filesystem=ntfs label=System Reserved modified_by_chkdsk=true mounted_on_nt4=true resize_log_file=true state=dirty upgrade_on_mount=true *-volume:1 description: Extended partition physical id: 2 bus info: scsi@0:0.0.0,2 logical name: /dev/sda2 size: 116GiB capacity: 116GiB capabilities: primary extended partitioned partitioned:extended *-logicalvolume:0 description: Linux swap / Solaris partition physical id: 5 logical name: /dev/sda5 capacity: 6037MiB capabilities: nofs *-logicalvolume:1 description: Linux filesystem partition physical id: 6 logical name: /dev/sda6 logical name: / capacity: 110GiB configuration: mount.fstype=ext4 mount.options=rw,relatime,errors=remount-ro,data=ordered state=mounted *-volume:2 description: Windows NTFS volume physical id: 3 bus info: scsi@0:0.0.0,3 logical name: /dev/sda3 logical name: /media/os version: 3.1 serial: 4e7853ec-5555-a74d-82e0-9f49798d3772 size: 156GiB capacity: 156GiB capabilities: primary ntfs initialized configuration: clustersize=4096 created=2013-09-19 09:19:00 filesystem=ntfs label=OS mount.fstype=fuseblk mount.options=ro,nosuid,nodev,relatime,user_id=0,group_id=0,allow_other,blksize=4096 state=mounted *-volume:3 description: Windows NTFS volume physical id: 4 bus info: scsi@0:0.0.0,4 logical name: /dev/sda4 logical name: /media/data version: 3.1 serial: 7666d55f-e1bf-e645-9791-2a1a31b24b9a size: 322GiB capacity: 322GiB capabilities: primary ntfs initialized configuration: clustersize=4096 created=2013-09-17 23:27:01 filesystem=ntfs label=Data modified_by_chkdsk=true mount.fstype=fuseblk mount.options=rw,nosuid,nodev,relatime,user_id=0,group_id=0,allow_other,blksize=4096 mounted_on_nt4=true resize_log_file=true state=mounted upgrade_on_mount=true *-scsi:1 physical id: 2 logical name: scsi1 capabilities: emulated *-cdrom description: DVD-RAM writer product: DVD+-RW GT32N vendor: HL-DT-ST physical id: 0.0.0 bus info: scsi@1:0.0.0 logical name: /dev/cdrom logical name: /dev/sr0 version: A201 capabilities: removable audio cd-r cd-rw dvd dvd-r dvd-ram configuration: ansiversion=5 status=nodisc *-battery product: DELL vendor: SANYO physical id: 1 version: 2008 serial: 1.0 slot: Rear capacity: 57720mWh configuration: voltage=11.1V `

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Amazon Web Services (AWS) Plug-in for Oracle Enterprise Manager

- by Anand Akela

v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Contributed by Sunil Kunisetty and Daniel Chan Introduction and ArchitectureAs more and more enterprises deploy some of their non-critical workload on Amazon Web Services (AWS), it’s becoming critical to monitor those public AWS resources along side with their on-premise resources. Oracle recently announced Oracle Enterprise Manager Plug-in for Amazon Web Services (AWS) allows you to achieve that goal. The on-premise Oracle Enterprise Manager (EM12c) acts as a single tool to get a comprehensive view of your public AWS resources as well as your private cloud resources. By deploying the plug-in within your Cloud Control environment, you gain the following management features: Monitor EBS, EC2 and RDS instances on Amazon Web Services Gather performance metrics and configuration details for AWS instances Raise alerts and violations based on thresholds set on monitoring Generate reports based on the gathered data Users of this Plug-in can leverage the rich Enterprise Manager features such as system promotion, incident generation based on thresholds, integration with 3rd party ticketing applications etc. AWS Monitoring via this Plug-in is enabled via Amazon CloudWatch API and the users of this Plug-in are responsible for supplying credentials for accessing AWS and the CloudWatch API. This Plug-in can only be deployed on an EM12C R2 platform and agent version should be at minimum 12c R2.Here is a pictorial view of the overall architecture: Amazon Elastic Block Store (EBS) Amazon Elastic Compute Cloud (EC2) Amazon Relational Database Service (RDS) Here are a few key features: Rich and exhaustive list of metrics. Metrics can be gathered from an Agent running outside AWS. Critical configuration information. Custom Home Pages with charts and AWS configuration information. Generate incidents based on thresholds set on monitoring data. Discovery and Monitoring AWS instances can be added to EM12C either via the EM12c User Interface (UI) or the EM12c Command Line Interface ( EMCLI) by providing the AWS credentials (Secret Key and Access Key Id) as well as resource specific properties as target properties. Here is a quick mapping of target types and properties for each AWS resources AWS Resource Type Target Type Resource specific properties EBS Resource Amazon EBS Service CloudWatch base URI, EC2 Base URI, Period, Volume Id, Proxy Server and Port EC2 Resource Amazon EC2 Service CloudWatch base URI, EC2 Base URI, Period, Instance Id, Proxy Server and Port RDS Resource Amazon RDS Service CloudWatch base URI, RDS Base URI, Period, Instance Id, Proxy Server and Port Proxy server and port are optional and are only needed if the agent is within the firewall. Here is an emcli example to add an EC2 target. Please read the Installation and Readme guide for more details and step-by-step instructions to deploy the plugin and adding the AWS the instances. ./emcli add_target \ -name="<target name>" \ -type="AmazonEC2Service" \ -host="<host>" \ -properties="ProxyHost=<proxy server>;ProxyPort=<proxy port>;EC2_BaseURI=http://ec2.<region>.amazonaws.com;BaseURI=http://monitoring.<region>.amazonaws.com;InstanceId=<EC2 instance Id>;Period=<data point periond>" \ -subseparator=properties="=" ./emcli set_monitoring_credential \ -set_name="AWSKeyCredentialSet" \ -target_name="<target name>" \ -target_type="AmazonEC2Service" \ -cred_type="AWSKeyCredential" \ -attributes="AccessKeyId:<access key id>;SecretKey:<secret key>" Emcli utility is found under the ORACLE_HOME of EM12C install. Once the instance is discovered, the target will show up under the ‘All Targets’ list under “Amazon EC2 Service’. Once the instances are added, one can navigate to the custom homepages for these resource types. The custom home pages not only include critical metrics, but also vital configuration parameters and incidents raised for these instances. By mapping the configuration parameters as instance properties, we can slice-and-dice and group various AWS instance by leveraging the EM12C Config search feature. The following configuration properties and metrics are collected for these Resource types. Resource Type Configuration Properties Metrics EBS Resource Volume Id, Volume Type, Device Name, Size, Availability Zone Response: Status Utilization: QueueLength, IdleTime Volume Statistics: ReadBrandwith, WriteBandwidth, ReadThroughput, WriteThroughput Operation Statistics: ReadSize, WriteSize, ReadLatency, WriteLatency EC2 Resource Instance ID, Owner Id, Root Device type, Instance Type. Availability Zone Response: Status CPU Utilization: CPU Utilization Disk I/O: DiskReadBytes, DiskWriteBytes, DiskReadOps, DiskWriteOps, DiskReadRate, DiskWriteRate, DiskIOThroughput, DiskReadOpsRate, DiskWriteOpsRate, DiskOperationThroughput Network I/O : NetworkIn, NetworkOut, NetworkInRate, NetworkOutRate, NetworkThroughput RDS Resource Instance ID, Database Engine Name, Database Engine Version, Database Instance Class, Allocated Storage Size, Availability Zone Response: Status Disk I/O: ReadIOPS, WriteIOPS, ReadLatency, WriteLatency, ReadThroughput, WriteThroughput DB Utilization: BinLogDiskUsage, CPUUtilization, DatabaseConnections, FreeableMemory, ReplicaLag, SwapUsage Custom Home Pages As mentioned above, we have custom home pages for these target types that include basic configuration information, last 24 hours availability, top metrics and the incidents generated. Here are few snapshots. EBS Instance Home Page: EC2 Instance Home Page: RDS Instance Home Page: Further Reading: 1) AWS Plugin download 2) Installation and Read Me. 3) Screenwatch on SlideShare 4) Extensibility Programmer's Guide 5) Amazon Web Services

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array and array_view from amp.h

- by Daniel Moth

This is a very long post, but it also covers what are probably the classes (well, array_view at least) that you will use the most with C++ AMP, so I hope you enjoy it! Overview The concurrency::array and concurrency::array_view template classes represent multi-dimensional data of type T, of N dimensions, specified at compile time (and you can later access the number of dimensions via the rank property). If N is not specified, it is assumed that it is 1 (i.e. single-dimensional case). They are rectangular (not jagged). The difference between them is that array is a container of data, whereas array_view is a wrapper of a container of data. So in that respect, array behaves like an STL container, whereas the closest thing an array_view behaves like is an STL iterator (albeit with random access and allowing you to view more than one element at a time!). The data in the array (whether provided at creation time or added later) resides on an accelerator (which is specified at creation time either explicitly by the developer, or set to the default accelerator at creation time by the runtime) and is laid out contiguously in memory. The data provided to the array_view is not stored by/in the array_view, because the array_view is simply a view over the real source (which can reside on the CPU or other accelerator). The underlying data is copied on demand to wherever the array_view is accessed. Elements which differ by one in the least significant dimension of the array_view are adjacent in memory. array objects must be captured by reference into the lambda you pass to the parallel_for_each call, whereas array_view objects must be captured by value (into the lambda you pass to the parallel_for_each call). Creating array and array_view objects and relevant properties You can create array_view objects from other array_view objects of the same rank and element type (shallow copy, also possible via assignment operator) so they point to the same underlying data, and you can also create array_view objects over array objects of the same rank and element type e.g. array_view<int,3> a(b); // b can be another array or array_view of ints with rank=3 Note: Unlike the constructors above which can be called anywhere, the ones in the rest of this section can only be called from CPU code. You can create array objects from other array objects of the same rank and element type (copy and move constructors) and from other array_view objects, e.g. array<float,2> a(b); // b can be another array or array_view of floats with rank=2 To create an array from scratch, you need to at least specify an extent object, e.g. array<int,3> a(myExtent);. Note that instead of an explicit extent object, there are convenience overloads when N<=3 so you can specify 1-, 2-, 3- integers (dependent on the array's rank) and thus have the extent created for you under the covers. At any point, you can access the array's extent thought the extent property. The exact same thing applies to array_view (extent as constructor parameters, incl. convenience overloads, and property). While passing only an extent object to create an array is enough (it means that the array will be written to later), it is not enough for the array_view case which must always wrap over some other container (on which it relies for storage space and actual content). So in addition to the extent object (that describes the shape you'd like to be viewing/accessing that data through), to create an array_view from another container (e.g. std::vector) you must pass in the container itself (which must expose .data() and a .size() methods, e.g. like std::array does), e.g. array_view<int,2> aaa(myExtent, myContainerOfInts); Similarly, you can create an array_view from a raw pointer of data plus an extent object. Back to the array case, to optionally initialize the array with data, you can pass an iterator pointing to the start (and optionally one pointing to the end of the source container) e.g. array<double,1> a(5, myVector.begin(), myVector.end()); We saw that arrays are bound to an accelerator at creation time, so in case you don’t want the C++ AMP runtime to assign the array to the default accelerator, all array constructors have overloads that let you pass an accelerator_view object, which you can later access via the accelerator_view property. Note that at the point of initializing an array with data, a synchronous copy of the data takes place to the accelerator, and then to copy any data back we'll see that an explicit copy call is required. This does not happen with the array_view where copying is on demand... refresh and synchronize on array_view Note that in the previous section on constructors, unlike the array case, there was no overload that accepted an accelerator_view for array_view. That is because the array_view is simply a wrapper, so the allocation of the data has already taken place before you created the array_view. When you capture an array_view variable in your call to parallel_for_each, the copy of data between the non-CPU accelerator and the CPU takes place on demand (i.e. it is implicit, versus the explicit copy that has to happen with the array). There are some subtleties to the on-demand-copying that we cover next. The assumption when using an array_view is that you will continue to access the data through the array_view, and not through the original underlying source, e.g. the pointer to the data that you passed to the array_view's constructor. So if you modify the data through the array_view on the GPU, the original pointer on the CPU will not "know" that, unless one of two things happen: you access the data through the array_view on the CPU side, i.e. using indexing that we cover below you explicitly call the array_view's synchronize method on the CPU (this also gets called in the array_view's destructor for you) Conversely, if you make a change to the underlying data through the original source (e.g. the pointer), the array_view will not "know" about those changes, unless you call its refresh method. Finally, note that if you create an array_view of const T, then the data is copied to the accelerator on demand, but it does not get copied back, e.g. array_view<const double, 5> myArrView(…); // myArrView will not get copied back from GPU There is also a similar mechanism to achieve the reverse, i.e. not to copy the data of an array_view to the GPU. copy_to, data, and global copy/copy_async functions Both array and array_view expose two copy_to overloads that allow copying them to another array, or to another array_view, and these operations can also be achieved with assignment (via the = operator overloads). Also both array and array_view expose a data method, to get a raw pointer to the underlying data of the array or array_view, e.g. float* f = myArr.data();. Note that for array_view, this only works when the rank is equal to 1, due to the data only being contiguous in one dimension as covered in the overview section. Finally, there are a bunch of global concurrency::copy functions returning void (and corresponding concurrency::copy_async functions returning a future) that allow copying between arrays and array_views and iterators etc. Just browse intellisense or amp.h directly for the full set. Note that for array, all copying described throughout this post is deep copying, as per other STL container expectations. You can never have two arrays point to the same data. indexing into array and array_view plus projection Reading or writing data elements of an array is only legal when the code executes on the same accelerator as where the array was bound to. In the array_view case, you can read/write on any accelerator, not just the one where the original data resides, and the data gets copied for you on demand. In both cases, the way you read and write individual elements is via indexing as described next. To access (or set the value of) an element, you can index into it by passing it an index object via the subscript operator. Furthermore, if the rank is 3 or less, you can use the function ( ) operator to pass integer values instead of having to use an index object. e.g. array<float,2> arr(someExtent, someIterator); //or array_view<float,2> arr(someExtent, someContainer); index<2> idx(5,4); float f1 = arr[idx]; float f2 = arr(5,4); //f2 ==f1 //and the reverse for assigning, e.g. arr(idx[0], 7) = 6.9; Note that for both array and array_view, regardless of rank, you can also pass a single integer to the subscript operator which results in a projection of the data, and (for both array and array_view) you get back an array_view of rank N-1 (or if the rank was 1, you get back just the element at that location). Not Covered In this already very long post, I am not going to cover three very cool methods (and related overloads) that both array and array_view expose: view_as, section, reinterpret_as. We'll revisit those at some point in the future, probably on the team blog. Comments about this post by Daniel Moth welcome at the original blog.

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256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

- by Alan Smith

For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour. This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005> define wooden texture { noise surface { ambient 0.2 diffuse 0.7 specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1 lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7 } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } } viewpoint { from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60 resolution 640, 480 aspect 1.6 image_format 0 } light <-10, 30, 20> light <-10, 30, -20> object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden } object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome } After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue } In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000 As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72 Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: · On-Premise o Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. · Windows Azure o Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment. The architecture of each worker role is shown below. The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" > <WorkerRole name="CloudRayWorkerRole" vmsize="Small"> <Imports> </Imports> <ConfigurationSettings> <Setting name="DataConnectionString" /> </ConfigurationSettings> <LocalResources> <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" /> </LocalResources> </WorkerRole> </ServiceDefinition> The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1. The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2. PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3. DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4. The JPG file is uploaded from local storage to the images blob container. 5. A message is placed on the images queue to indicate a new image is available for download. 6. The job message is deleted from the job queue. 7. The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() { // Set environment variables string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation); string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation); LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder"); string localStorageRootPath = rayStorage.RootPath; JobQueue jobQueue = new JobQueue("renderjobs"); JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs"); CloudRayBlob sceneBlob = new CloudRayBlob("scenes"); CloudRayBlob imageBlob = new CloudRayBlob("images"); RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource(); Frames = 0; while (true) { // Get the render job from the queue CloudQueueMessage jobMsg = jobQueue.Get(); if (jobMsg != null) { // Get the file details string sceneFile = jobMsg.AsString; string tgaFile = sceneFile.Replace(".pi", ".tga"); string jpgFile = sceneFile.Replace(".pi", ".jpg"); string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile); string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile); string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile); // Copy the scene file to local storage sceneBlob.DownloadFile(sceneFilePath); // Run the ray tracer. string polyrayArguments = string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath); Process polyRayProcess = new Process(); polyRayProcess.StartInfo.FileName = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath); polyRayProcess.StartInfo.Arguments = polyrayArguments; polyRayProcess.Start(); polyRayProcess.WaitForExit(); // Convert the image string dtaArguments = string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath)); Process dtaProcess = new Process(); dtaProcess.StartInfo.FileName = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath); dtaProcess.StartInfo.Arguments = dtaArguments; dtaProcess.Start(); dtaProcess.WaitForExit(); // Upload the image to blob storage imageBlob.UploadFile(jpgFilePath); // Add a download job. downloadQueue.Add(jpgFile); // Delete the render job message jobQueue.Delete(jobMsg); Frames++; } else { Thread.Sleep(1000); } // Log the worker role activity. roleLifecycleDataSource.Alive ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames); } } Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage. public class RoleLifecycle : TableServiceEntity { public string ServerName { get; set; } public string Status { get; set; } public DateTime StartTime { get; set; } public DateTime EndTime { get; set; } public long SecondsRunning { get; set; } public DateTime LastActiveTime { get; set; } public int Frames { get; set; } public string Comment { get; set; } public RoleLifecycle() { } public RoleLifecycle(string roleName) { PartitionKey = roleName; RowKey = Utils.GetAscendingRowKey(); Status = "Started"; StartTime = DateTime.UtcNow; LastActiveTime = StartTime; EndTime = StartTime; SecondsRunning = 0; Frames = 0; } } A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*"> <Role name="CloudRayWorkerRole"> <Instances count="16" /> <ConfigurationSettings> <Setting name="DataConnectionString" value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." /> </ConfigurationSettings> </Role> </ServiceConfiguration> About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames. Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation. With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes. At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*"> <Role name="CloudRayWorkerRole"> <Instances count="256" /> <ConfigurationSettings> <Setting name="DataConnectionString" value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." /> </ConfigurationSettings> </Role> </ServiceConfiguration> Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames. Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds. We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes. The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each. Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. · Windows Azure can provide massive compute power, on demand, in a matter of minutes. · The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. · Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. · No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. · Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. · Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. · Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. · Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. · If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. · Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. · Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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Using R to Analyze G1GC Log Files

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Using R to Analyze G1GC Log Files Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z,]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\$ " , " " ); gsub ( "\ $" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period: par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight. plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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Debian virtual memory reaching limit

- by Gregor

As a relative newbie to systems, I inherited a Debian server and I've noticed that virtual memory is very high (around 95%!). The server has been running slow for around 6 months, and I was wondering if any of you had any tips on things I could try, particularly on freeing up memory. The server hosts various websites and also a Postit email server. Here are the details: Operating system Debian Linux 5.0 Webmin version 1.580 Time on system Thu Apr 12 11:12:21 2012 Kernel and CPU Linux 2.6.18-6-amd64 on x86_64 Processor information Intel(R) Core(TM)2 Duo CPU E7400 @ 2.80GHz, 2 cores System uptime 229 days, 12 hours, 50 minutes Running processes 138 CPU load averages 0.10 (1 min) 0.28 (5 mins) 0.36 (15 mins) CPU usage 14% user, 1% kernel, 0% IO, 85% idle Real memory 2.94 GB total, 1.69 GB used Virtual memory 3.93 GB total, 3.84 GB used Local disk space 142.84 GB total, 116.13 GB used Free m output: free -m total used free shared buffers cached Mem: 3010 2517 492 0 107 996 -/+ buffers/cache: 1413 1596 Swap: 4024 3930 93 Top output: top - 11:59:57 up 229 days, 13:38, 1 user, load average: 0.26, 0.24, 0.26 Tasks: 136 total, 2 running, 134 sleeping, 0 stopped, 0 zombie Cpu(s): 3.8%us, 0.5%sy, 0.0%ni, 95.0%id, 0.7%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 3082544k total, 2773160k used, 309384k free, 111496k buffers Swap: 4120632k total, 4024712k used, 95920k free, 1036136k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 28796 www-data 16 0 304m 68m 6188 S 8 2.3 0:03.13 apache2 1 root 15 0 10304 592 564 S 0 0.0 0:00.76 init 2 root RT 0 0 0 0 S 0 0.0 0:04.06 migration/0 3 root 34 19 0 0 0 S 0 0.0 0:05.67 ksoftirqd/0 4 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/0 5 root RT 0 0 0 0 S 0 0.0 0:00.06 migration/1 6 root 34 19 0 0 0 S 0 0.0 0:01.26 ksoftirqd/1 7 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/1 8 root 10 -5 0 0 0 S 0 0.0 0:00.12 events/0 9 root 10 -5 0 0 0 S 0 0.0 0:00.00 events/1 10 root 10 -5 0 0 0 S 0 0.0 0:00.00 khelper 11 root 10 -5 0 0 0 S 0 0.0 0:00.02 kthread 16 root 10 -5 0 0 0 S 0 0.0 0:15.51 kblockd/0 17 root 10 -5 0 0 0 S 0 0.0 0:01.32 kblockd/1 18 root 15 -5 0 0 0 S 0 0.0 0:00.00 kacpid 127 root 10 -5 0 0 0 S 0 0.0 0:00.00 khubd 129 root 10 -5 0 0 0 S 0 0.0 0:00.00 kseriod 180 root 10 -5 0 0 0 S 0 0.0 70:09.05 kswapd0 181 root 17 -5 0 0 0 S 0 0.0 0:00.00 aio/0 182 root 17 -5 0 0 0 S 0 0.0 0:00.00 aio/1 780 root 16 -5 0 0 0 S 0 0.0 0:00.00 ata/0 782 root 16 -5 0 0 0 S 0 0.0 0:00.00 ata/1 783 root 16 -5 0 0 0 S 0 0.0 0:00.00 ata_aux 802 root 10 -5 0 0 0 S 0 0.0 0:00.00 scsi_eh_0 803 root 10 -5 0 0 0 S 0 0.0 0:00.00 scsi_eh_1 804 root 10 -5 0 0 0 S 0 0.0 0:00.00 scsi_eh_2 805 root 10 -5 0 0 0 S 0 0.0 0:00.00 scsi_eh_3 1013 root 10 -5 0 0 0 S 0 0.0 49:27.78 kjournald 1181 root 15 -4 16912 452 448 S 0 0.0 0:00.05 udevd 1544 root 14 -5 0 0 0 S 0 0.0 0:00.00 kpsmoused 1706 root 13 -5 0 0 0 S 0 0.0 0:00.00 kmirrord 1995 root 18 0 193m 3324 1688 S 0 0.1 8:52.77 rsyslogd 2031 root 15 0 48856 732 608 S 0 0.0 0:01.86 sshd 2071 root 25 0 17316 1072 1068 S 0 0.0 0:00.00 mysqld_safe 2108 mysql 15 0 320m 72m 4368 S 0 2.4 1923:25 mysqld 2109 root 18 0 3776 500 496 S 0 0.0 0:00.00 logger 2180 postgres 15 0 99504 3016 2880 S 0 0.1 1:24.15 postgres 2184 postgres 15 0 99504 3596 3420 S 0 0.1 0:02.08 postgres 2185 postgres 15 0 99504 696 628 S 0 0.0 0:00.65 postgres 2186 postgres 15 0 99640 892 648 S 0 0.0 0:01.18 postgres

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Hard drive write speed - finding a lighter antivirus?

- by Shingetsu

I recently have been getting a lot of system lag here (for example, the mouse and the display in general take about 15 seconds to react in the worst cases). After a lot of monitoring the resources, I found that the problem mainly happens when too much Disk I/O is being done. Three culprits have been identified: My browser had the highest write I/O with 35,000,000 I/O Write Bytes. Steam had the highest read I/O (when IDLE!!!) with 106,000,000 I/O Read Bytes. My antivirus (in both cases I will soon mention) was the runner up in both cases with: 30,000,000ish write and 80,000,000ish read. The first AV I had was Avast! which I had liked on my previous system. After noticing it taking so much I/O I switched to Panda (supposing it wouldn't use TOO much during idle phase). However it only used a bit less I/O. Just a lot less memory and cpu and somewhat more network. My browser at the moment is Maxthon 3 (which I like a lot). Before this I was running chrome which had similar data and much higher cpu when running in the background was enabled. I'm not going to be running steam all the time and there aren't many alternatives to it. I like my browser very much, but I AM willing to switch if there's an obvious problem (I'm in programming, however I'm not a very good sysadmin, especially not when it comes to windows). Finally, my system almost stops lagging when I turn off the antivirus (and preferably steam) (some remains but once in every 5-6 hours for a few seconds so it isn't a big problem). My question (has a few parts): Is it possible to configure steam to lower it's I/O usage? (and maybe network while we're at it?) Which antivirus (very preferably free) uses lowest I/O while idle (I leave PC alone during active scans so that isn't a problem). Is there an obvious problem with my current browser and, if so, is there a way to fix it or should I switch and, if so, to what? (P.S. I've been on FFox for some time too). Info on system: Windows 7 (32 bit T_T, I am getting a new one in a few months but I want to keep using system during that time though). Hard Drive (main) is a Raid0. (Also have an external 1TB one which contains steam (and steam alone). As such it doesn't get used by much anything other than steam and isn't a very large problem. However steam still uses some I/O of registry) CPU: Intel(R) Core(TM)2 CPU [email protected] RAM: 6GB (3.25GB usable) (this and CPU have little effect as shown in next section) Additional info: Memory usage during problematic times: 44% CPU usage during problematic times: 35% Page File: main drive: system managed. 1TB drive: none. The current system I'm using is about 6 years old and is mainly a place holder while I await the new one in a few months. Final words: this is my 1st post on Super User (this question wouldn't feel right on Stack Overflow where I usually stay). If it doesn't have it's place here please tell me. If anything is wrong with it, same. Edit Technically I'm looking for a live thread detection program with minimal IO usage. I already have good active scan capability: Kaspersky (the free scanner uses the paid database) and MalwareBytes. Edit 2 Noticed another one, it seems that windows media player has been using stuff even when off! Turning it off and restarting now. If the problem is fixed I'll tell you guys. The reason I didn't notice it before was because I didn't have resource manager in front of me at the MOMENT of the problem. Now I did and it was at the very top of the list!

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How to get more information from the system crash

- by viraptor

I'd like to debug an issue I'm having with a linux (debian stable) server, but I'm running out of ideas of how to confirm any diagnosis. Some background: The servers are running DL160 class with hardware raid between two disks. They're running a lot of services, mostly utilising network interface and CPU. There are 8 cpus and 7 "main" most cpu-hungry processes are bound to one core each via cpu affinity. Other random background scripts are not forced anywhere. The filesystem is writing ~1.5k blocks/s the whole time (goes up above 2k/s in peak times). Normal CPU usage for those servers is ~60% on 7 cores and some minimal usage on the last (whatever's running on shells usually). What actually happens is that the "main" services start using 100% CPU at some point, mainly stuck in kernel time. After a couple of seconds, LA goes over 400 and we lose any way to connect to the box (KVM is on it's way, but not there yet). Sometimes we see a kernel reporting hung task (but not always): [118951.272884] INFO: task zsh:15911 blocked for more than 120 seconds. [118951.272955] "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. [118951.273037] zsh D 0000000000000000 0 15911 1 [118951.273093] ffff8101898c3c48 0000000000000046 0000000000000000 ffffffffa0155e0a [118951.273183] ffff8101a753a080 ffff81021f1c5570 ffff8101a753a308 000000051f0fd740 [118951.273274] 0000000000000246 0000000000000000 00000000ffffffbd 0000000000000001 [118951.273335] Call Trace: [118951.273424] [<ffffffffa0155e0a>] :ext3:__ext3_journal_dirty_metadata+0x1e/0x46 [118951.273510] [<ffffffff804294f6>] schedule_timeout+0x1e/0xad [118951.273563] [<ffffffff8027577c>] __pagevec_free+0x21/0x2e [118951.273613] [<ffffffff80428b0b>] wait_for_common+0xcf/0x13a [118951.273692] [<ffffffff8022c168>] default_wake_function+0x0/0xe .... This would point at raid / disk failure, however sometimes the tasks are hung on kernel's gettsc which would indicate some general weird hardware behaviour. It's also running mysql (almost read-only, 99% cache hit), which seems to spawn a lot more threads during the system problems. During the day it does ~200kq/s (selects) and ~10q/s (writes). The host is never running out of memory or swapping, no oom reports are spotted. We've got many boxes with similar/same hardware and they all seem to behave that way, but I'm not sure which part fails, so it's probably not a good idea to just grab something more powerful and hope the problem goes away. Applications themselves don't really report anything wrong when they're running. I can run anything safely on the same hardware in an isolated environment. What can I do to narrow down the problem? Where else should I look for explanation?

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Linux per-process resource limits - a deep Red Hat Mystery

- by BobBanana

I have my own multithreaded C program which scales in speed smoothly with the number of CPU cores.. I can run it with 1, 2, 3, etc threads and get linear speedup.. up to about 5.5x speed on a 6-core CPU on a Ubuntu Linux box. I had an opportunity to run the program on a very high end Sunfire x4450 with 4 quad-core Xeon processors, running Red Hat Enterprise Linux. I was eagerly anticipating seeing how fast the 16 cores could run my program with 16 threads.. But it runs at the same speed as just TWO threads! Much hair-pulling and debugging later, I see that my program really is creating all the threads, they really are running simultaneously, but the threads themselves are slower than they should be. 2 threads runs about 1.7x faster than 1, but 3, 4, 8, 10, 16 threads all run at just net 1.9x! I can see all the threads are running (not stalled or sleeping), they're just slow. To check that the HARDWARE wasn't at fault, I ran SIXTEEN copies of my program independently, simultaneously. They all ran at full speed. There really are 16 cores and they really do run at full speed and there really is enough RAM (in fact this machine has 64GB, and I only use 1GB per process). So, my question is if there's some OPERATING SYSTEM explanation, perhaps some per-process resource limit which automatically scales back thread scheduling to keep one process from hogging the machine. Clues are: My program does not access the disk or network. It's CPU limited. Its speed scales linearly on a single CPU box in Ubuntu Linux with a hexacore i7 for 1-6 threads. 6 threads is effectively 6x speedup. My program never runs faster than 2x speedup on this 16 core Sunfire Xeon box, for any number of threads from 2-16. Running 16 copies of my program single threaded runs perfectly, all 16 running at once at full speed. top shows 1600% of CPUs allocated. /proc/cpuinfo shows all 16 cores running at full 2.9GHz speed (not low frequency idle speed of 1.6GHz) There's 48GB of RAM free, it is not swapping. What's happening? Is there some process CPU limit policy? How could I measure it if so? What else could explain this behavior? Thanks for your ideas to solve this, the Great Xeon Slowdown Mystery of 2010!

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ASP.NET Asynchronous Pages and when to use them

- by rajbk

There have been several articles posted about using asynchronous pages in ASP.NET but none of them go into detail as to when you should use them. I finally found a great post by Thomas Marquardt that explains the process in depth. He addresses a key misconception also: So, in your ASP.NET application, when should you perform work asynchronously instead of synchronously? Well, only 1 thread per CPU can execute at a time. Did you catch that? A lot of people seem to miss this point...only one thread executes at a time on a CPU. When you have more than this, you pay an expensive penalty--a context switch. However, if a thread is blocked waiting on work...then it makes sense to switch to another thread, one that can execute now. It also makes sense to switch threads if you want work to be done in parallel as opposed to in series, but up until a certain point it actually makes much more sense to execute work in series, again, because of the expensive context switch. Pop quiz: If you have a thread that is doing a lot of computational work and using the CPU heavily, and this takes a while, should you switch to another thread? No! The current thread is efficiently using the CPU, so switching will only incur the cost of a context switch. Ok, well, what if you have a thread that makes an HTTP or SOAP request to another server and takes a long time, should you switch threads? Yes! You can perform the HTTP or SOAP request asynchronously, so that once the "send" has occurred, you can unwind the current thread and not use any threads until there is an I/O completion for the "receive". Between the "send" and the "receive", the remote server is busy, so locally you don't need to be blocking on a thread, but instead make use of the asynchronous APIs provided in .NET Framework so that you can unwind and be notified upon completion. Again, it only makes sense to switch threads if the benefit from doing so out weights the cost of the switch. Read more about it in these posts: Performing Asynchronous Work, or Tasks, in ASP.NET Applications http://blogs.msdn.com/tmarq/archive/2010/04/14/performing-asynchronous-work-or-tasks-in-asp-net-applications.aspx ASP.NET Thread Usage on IIS 7.0 and 6.0 http://blogs.msdn.com/tmarq/archive/2007/07/21/asp-net-thread-usage-on-iis-7-0-and-6-0.aspx PS: I generally do not write posts that simply link to other posts but think it is warranted in this case.

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Java FAQ: Tudo o que você precisa saber

- by Bruno.Borges

Com frequência recebo e-mails de clientes com dúvidas sobre "quando sairá a próxima versão do Java?", ou então "quando vai expirar o Java?" ou ainda "quais as mudanças da próxima versão?". Por isso resolvi escrever aqui um FAQ, respondendo estas dúvidas e muitas outras. Este post estará sempre atualizado, então se você possui alguma dúvida, envie para mim no Twitter @brunoborges. Qual a diferença entre o Oracle JDK e o OpenJDK?O projeto OpenJDK funciona como a implementação de referência Open Source do Java Standard Edition. Empresas como a Oracle, IBM, e Azul Systems suportam e investem no projeto OpenJDK para continuar evoluindo a plataforma Java. O Oracle JDK é baseado no OpenJDK, mas traz outras ferramentas como o Mission Control, e a máquina virtual traz algumas features avançadas como por exemplo o Flight Recorder. Até a versão 6, a Oracle oferecia duas máquinas virtuais: JRockit (BEA) e HotSpot (Sun). A partir da versão 7 a Oracle unificou as máquinas virtuais, e levou as features avançadas do JRockit para dentro da VM HotSpot. Leia também o OpenJDK FAQ. Onde posso obter binários beta Early Access do JDK 7, JDK 8, JDK 9 para testar?A partir do projeto OpenJDK, existe um projeto específico para cada versão do Java. Nestes projetos você pode encontrar binários beta Early Access, além do código-fonte. JDK 6 - http://jdk6.java.net/ JDK 7 - http://jdk7.java.net/ JDK 8 - http://jdk8.java.net/ JDK 9 - http://jdk9.java.net/ Quando acaba o suporte do Oracle Java SE 6, 7, 8? Somente produtos e versões com release oficial são suportados pela Oracle (exemplo: não há suporte para binários beta do JDK 7, JDK 8, ou JDK 9). Existem duas categorias de datas que o usuriário do Java deve estar ciente: EOPU - End of Public UpdatesMomento em que a Oracle não mais disponibiliza publicamente atualizações Oracle SupportPolítica de suporte da Oracle para produtos, incluindo o Oracle Java SE O Oracle Java SE é um produto e portando os períodos de suporte são regidos pelo Oracle Lifetime Support Policy. Consulte este documento para datas atualizadas e específicas para cada versão do Java. O Oracle Java SE 6 já atingiu EOPU (End of Public Updates) e agora é mantido e atualizado somente para clientes através de contrato comercial de suporte. Para maiores informações, consulte a página sobre Oracle Java SE Support. O mais importante aqui é você estar ciente sobre as datas de EOPU para as versões do Java SE da Oracle.Consulte a página do Oracle Java SE Support Roadmap e busque nesta página pela tabela com nome Java SE Public Updates. Nela você encontrará a data em que determinada versão do Java irá atingir EOPU. Como funciona o versionamento do Java?Em 2013, a Oracle divulgou um novo esquema de versionamento do Java para facilmente identificar quando é um release CPU e quando é um release LFR, e também para facilitar o planejamento e desenvolvimento de correções e features para futuras versões. CPU - Critical Patch UpdateAtualizações com correções de segurança. Versão será múltipla de 5, ou com soma de 1 para manter o número ímpar. Exemplos: 7u45, 7u51, 7u55. LFR - Limited Feature ReleaseAtualizações com correções de funcionalidade, melhorias de performance, e novos recursos. Versões de números pares múltiplos de 20, com final 0. Exemplos: 7u40, 7u60, 8u20. Qual a data da próxima atualização de segurança (CPU) do Java SE?Lançamentos do tipo CPU são controlados e pré-agendados pela Oracle e se aplicam a todos os produtos, inclusive o Oracle Java SE. Estes releases acontecem a cada 3 meses, sempre na Terça-feira mais próxima do dia 17 dos meses de Janeiro, Abril, Julho, e Outubro. Consulte a página Critical Patch Updates, Security Alerts and Third Party Bulleting para saber das próximas datas. Caso tenha interesse, você pode acompanhar através de recebimentos destes boletins diretamente no seu email. Veja como assinar o Boletim de Segurança da Oracle. Qual a data da próxima atualização de features (LFR) do Java SE?A Oracle reserva o direito de não divulgar estas datas, assim como o faz para todos os seus produtos. Entretanto é possível acompanhar o desenvolvimento da próxima versão pelos sites do projeto OpenJDK. A próxima versão do JDK 7 será o update 60 e binários beta Early Access já estão disponíveis para testes. A próxima versão doJDK 8 será o update 20 e binários beta Early Access já estão disponíveis para testes. Onde posso ver as mudanças e o que foi corrigido para a próxima versão do Java?A Oracle disponibiliza um changelog para cada binário beta Early Access divulgado no portal Java.net. JDK 7 update 60 changelogs JDK 8 update 20 changelogs Quando o Java da minha máquina (ou do meu usuário) vai expirar?Conheçendo o sistema de versionamento do Java e a periodicidade dos releases de CPU, o usuário pode determinar quando que um update do Java irá expirar. De todo modo, a cada novo update, a Oracle já informa quando que este update deverá expirar diretamente no release notes da versão. Por exemplo, no release notes da versão Oracle Java SE 7 update 55, está escrito na seção JRE Expiration Date o seguinte: The JRE expires whenever a new release with security vulnerability fixes becomes available. Critical patch updates, which contain security vulnerability fixes, are announced one year in advance on Critical Patch Updates, Security Alerts and Third Party Bulletin. This JRE (version 7u55) will expire with the release of the next critical patch update scheduled for July 15, 2014. For systems unable to reach the Oracle Servers, a secondary mechanism expires this JRE (version 7u55) on August 15, 2014. After either condition is met (new release becoming available or expiration date reached), the JRE will provide additional warnings and reminders to users to update to the newer version. For more information, see JRE Expiration Date.Ou seja, a versão 7u55 irá expirar com o lançamento do próximo release CPU, pré-agendado para o dia 15 de Julho de 2014. E caso o computador do usuário não possa se comunicar com o servidor da Oracle, esta versão irá expirar forçadamente no dia 15 de Agosto de 2014 (através de um mecanismo embutido na versão 7u55). O usuário não é obrigado a atualizar para versões LFR e portanto, mesmo com o release da versão 7u60, a versão atual 7u55 não irá expirar.Veja o release notes do Oracle Java SE 8 update 5. Encontrei um bug. Como posso reportar bugs ou problemas no Java SE, para a Oracle?Sempre que possível, faça testes com os binários beta antes da versão final ser lançada. Qualquer problema que você encontrar com estes binários beta, por favor descreva o problema através do fórum de Project Feebdack do JDK.Caso você encontre algum problema em uma versão final do Java, utilize o formulário de Bug Report. Importante: bugs reportados por estes sistemas não são considerados Suporte e portanto não há SLA de atendimento. A Oracle reserva o direito de manter o bug público ou privado, e também de informar ou não o usuário sobre o progresso da resolução do problema. Tenho uma dúvida que não foi respondida aqui. Como faço?Se você possui uma pergunta que não foi respondida aqui, envie para bruno.borges_at_oracle.com e caso ela seja pertinente, tentarei responder neste artigo. Para outras dúvidas, entre em contato pelo meu Twitter @brunoborges.

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Both 12.10&12.04 Installation freeze

- by Fih

A friend of mine is having problems installing Ubuntu. We've tried both 12.10 and 12.04 ver. but each time we get http://img513.imageshack.us/img513/6332/27456089.png and then we got stuck. His comp is: Motherboard: ASUS P5G41-M LX CPU: Pentium(R) Dual-Core CPU E6500 @ 2.93GHz DISK: Disk 500.1 GB SAMSUNG HD502HJ RAM: Slot 1 DDR2 (PC2-6400) 2048 MB Kingston Slot 2 DDR2 (PC2-6400) 2048 MB Hyundai Electronics Graph card: NVIDIA GeForce GT 240 Any solution to this? Bests, Dwig

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