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  • C#: A "Dumbed-Down" C++?

    - by James Michael Hare
    I was spending a lovely day this last weekend watching my sons play outside in one of the better weekends we've had here in Saint Louis for quite some time, and whilst watching them and making sure no limbs were broken or eyes poked out with sticks and other various potential injuries, I was perusing (in the correct sense of the word) this month's MSDN magazine to get a sense of the latest VS2010 features in both IDE and in languages. When I got to the back pages, I saw a wonderful article by David S. Platt entitled, "In Praise of Dumbing Down"  (msdn.microsoft.com/en-us/magazine/ee336129.aspx).  The title captivated me and I read it and found myself agreeing with it completely especially as it related to my first post on divorcing C++ as my favorite language. Unfortunately, as Mr. Platt mentions, the term dumbing-down has negative connotations, but is really and truly a good thing.  You are, in essence, taking something that is extremely complex and reducing it to something that is much easier to use and far less error prone.  Adding safeties to power tools and anti-kick mechanisms to chainsaws are in some sense "dumbing them down" to the common user -- but that also makes them safer and more accessible for the common user.  This was exactly my point with C++ and C#.  I did not mean to infer that C++ was not a useful or good language, but that in a very high percentage of cases, is too complex and error prone for the job at hand. Choosing the correct programming language for a job is a lot like choosing any other tool for a task.  For example: if I want to dig a French drain in my lawn, I can attempt to use a huge tractor-like backhoe and the job would be done far quicker than if I would dig it by hand.  I can't deny that the backhoe has the raw power and speed to perform.  But you also cannot deny that my chances of injury or chances of severing utility lines or other resources climb at an exponential rate inverse to the amount of training I may have on that machinery. Is C++ a powerful tool?  Oh yes, and it's great for those tasks where speed and performance are paramount.  But for most of us, it's the wrong tool.  And keep in mind, I say this even though I have 17 years of experience in using it and feel myself highly adept in utilizing its features both in the standard libraries, the STL, and in supplemental libraries such as BOOST.  Which, although greatly help with adding powerful features quickly, do very little to curb the relative dangers of the language. So, you may say, the fault is in the developer, that if the developer had some higher skills or if we only hired C++ experts this would not be an issue.  Now, I will concede there is some truth to this.  Obviously, the higher skilled C++ developers you hire the better the chance they will produce highly performant and error-free code.  However, what good is that to the average developer who cannot afford a full stable of C++ experts? That's my point with C#:  It's like a kinder, gentler C++.  It gives you nearly the same speed, and in many ways even more power than C++, and it gives you a much softer cushion for novices to fall against if they code less-than-optimally.  A bug is a bug, of course, in any language, but C# does a good job of hiding and taking on the task of handling almost all of the resource issues that make C++ so tricky.  For my money, C# is much more maintainable, more feature-rich, second only slightly in performance, faster to market, and -- last but not least -- safer and easier to use.  That's why, where I work, I much prefer to see the developers moving to C#.  The quantity of bugs is much lower, and we don't need to hire "experts" to achieve the same results since the language itself handles those resource pitfalls so prevalent in poorly written C++ code.  C++ will still have its place in the world, and I'm sure I'll still use it now and again where it is truly the correct tool for the job, but for nearly every other project C# is a wonderfully "dumbed-down" version of C++ -- in the very best sense -- and to me, that's the smart choice.

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  • Slow Ubuntu 10.04 after long time unused

    - by Winston Ewert
    I'm at spring break so I'm back at my parent's house. I've turned my computer on which has been off since January and its unusably slow. This was not the case when I last used the computer in January. It is running 10.04, Memory: 875.5 MB CPU: AMD Athlon 64 X2 Dual Core Processor 4400+ Available Disk Space: 330.8 GB I'm not seeing a large usage of either memory or Disk I/O. If I look at my list of processes there is only a very small amount of CPU usage. However, if I hover over the CPU usage graph that I've on the top bar, I sometimes get really high readings like 100%. It took a long time to boot, to open firefox, to open a link in firefox. As far as I can tell everything that the computer tries to do is just massively slow. Right now, I'm apt-get dist-upgrading to install any updates that I will have missed since last time this computer was on. Any ideas as to what is going on here? UPDATE: I thought to check dmesg and it has a lot of entries like this: [ 1870.142201] ata3.00: exception Emask 0x0 SAct 0x7 SErr 0x0 action 0x0 [ 1870.142206] ata3.00: irq_stat 0x40000008 [ 1870.142210] ata3.00: failed command: READ FPDMA QUEUED [ 1870.142217] ata3.00: cmd 60/08:10:c0:4a:65/00:00:03:00:00/40 tag 2 ncq 4096 in [ 1870.142218] res 41/40:00:c5:4a:65/00:00:03:00:00/40 Emask 0x409 (media error) <F> [ 1870.142221] ata3.00: status: { DRDY ERR } [ 1870.142223] ata3.00: error: { UNC } [ 1870.143981] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1870.146758] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1870.146761] ata3.00: configured for UDMA/133 [ 1870.146777] ata3: EH complete [ 1872.092269] ata3.00: exception Emask 0x0 SAct 0x7 SErr 0x0 action 0x0 [ 1872.092274] ata3.00: irq_stat 0x40000008 [ 1872.092278] ata3.00: failed command: READ FPDMA QUEUED [ 1872.092285] ata3.00: cmd 60/08:00:c0:4a:65/00:00:03:00:00/40 tag 0 ncq 4096 in [ 1872.092287] res 41/40:00:c5:4a:65/00:00:03:00:00/40 Emask 0x409 (media error) <F> [ 1872.092289] ata3.00: status: { DRDY ERR } [ 1872.092292] ata3.00: error: { UNC } [ 1872.094050] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1872.096795] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1872.096798] ata3.00: configured for UDMA/133 [ 1872.096814] ata3: EH complete [ 1874.042279] ata3.00: exception Emask 0x0 SAct 0x7 SErr 0x0 action 0x0 [ 1874.042285] ata3.00: irq_stat 0x40000008 [ 1874.042289] ata3.00: failed command: READ FPDMA QUEUED [ 1874.042296] ata3.00: cmd 60/08:10:c0:4a:65/00:00:03:00:00/40 tag 2 ncq 4096 in [ 1874.042297] res 41/40:00:c5:4a:65/00:00:03:00:00/40 Emask 0x409 (media error) <F> [ 1874.042300] ata3.00: status: { DRDY ERR } [ 1874.042302] ata3.00: error: { UNC } [ 1874.044048] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1874.046837] ata3.00: SB600 AHCI: limiting to 255 sectors per cmd [ 1874.046840] ata3.00: configured for UDMA/133 [ 1874.046861] sd 2:0:0:0: [sda] Unhandled sense code [ 1874.046863] sd 2:0:0:0: [sda] Result: hostbyte=DID_OK driverbyte=DRIVER_SENSE [ 1874.046867] sd 2:0:0:0: [sda] Sense Key : Medium Error [current] [descriptor] [ 1874.046872] Descriptor sense data with sense descriptors (in hex): [ 1874.046874] 72 03 11 04 00 00 00 0c 00 0a 80 00 00 00 00 00 [ 1874.046883] 03 65 4a c5 [ 1874.046886] sd 2:0:0:0: [sda] Add. Sense: Unrecovered read error - auto reallocate failed [ 1874.046892] sd 2:0:0:0: [sda] CDB: Read(10): 28 00 03 65 4a c0 00 00 08 00 [ 1874.046900] end_request: I/O error, dev sda, sector 56969925 [ 1874.046920] ata3: EH complete I'm not certain, but that looks like my problem may be a failing hard drive. But the drive is less then a year old, it really shouldn't be failing now...

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Good DBAs Do Baselines

    - by Louis Davidson
    One morning, you wake up and feel funny. You can’t quite put your finger on it, but something isn’t quite right. What now? Unless you happen to be a hypochondriac, you likely drag yourself out of bed, get on with the day and gather more “evidence”. You check your symptoms over the next few days; do you feel the same, better, worse? If better, then great, it was some temporal issue, perhaps caused by an allergic reaction to some suspiciously spicy chicken. If the same or worse then you go to the doctor for some health advice, but armed with some data to share, and having ruled out certain possible causes that are fixed with a bit of rest and perhaps an antacid. Whether you realize it or not, in comparing how you feel one day to the next, you have taken baseline measurements. In much the same way, a DBA uses baselines to gauge the gauge health of their database servers. Of course, while SQL Server is very willing to share data regarding its health and activities, it has almost no idea of the difference between good and bad. Over time, experienced DBAs develop “mental” baselines with which they can gauge the health of their servers almost as easily as their own body. They accumulate knowledge of the daily, natural state of each part of their database system, and so know instinctively when one of their databases “feels funny”. Equally, they know when an “issue” is just a passing tremor. They see their SQL Server with all of its four CPU cores running close 100% and don’t panic anymore. Why? It’s 5PM and every day the same thing occurs when the end-of-day reports, which are very CPU intensive, are running. Equally, they know when they need to respond in earnest when it is the first time they have heard about an issue, even if it has been happening every day. Nevertheless, no DBA can retain mental baselines for every characteristic of their systems, so we need to collect physical baselines too. In my experience, surprisingly few DBAs do this very well. Part of the problem is that SQL Server provides a lot of instrumentation. If you look, you will find an almost overwhelming amount of data regarding user activity on your SQL Server instances, and use and abuse of the available CPU, I/O and memory. It seems like a huge task even to work out which data you need to collect, let alone start collecting it on a regular basis, managing its storage over time, and performing detailed comparative analysis. However, without baselines, though, it is very difficult to pinpoint what ails a server, just by looking at a single snapshot of the data, or to spot retrospectively what caused the problem by examining aggregated data for the server, collected over many months. It isn’t as hard as you think to get started. You’ve probably already established some troubleshooting queries of the type SELECT Value FROM SomeSystemTableOrView. Capturing a set of baseline values for such a query can be as easy as changing it as follows: INSERT into BaseLine.SomeSystemTable (value, captureTime) SELECT Value, SYSDATETIME() FROM SomeSystemTableOrView; Of course, there are monitoring tools that will collect and manage this baseline data for you, automatically, and allow you to perform comparison of metrics over different periods. However, to get yourself started and to prove to yourself (or perhaps the person who writes the checks for tools) the value of baselines, stick something similar to the above query into an agent job, running every hour or so, and you are on your way with no excuses! Then, the next time you investigate a slow server, and see x open transactions, y users logged in, and z rows added per hour in the Orders table, compare to your baselines and see immediately what, if anything, has changed!

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  • How to get faster graphics in KVM? VNC is painfully slow with Haiku OS guest, Spice won't install and SDL doesn't work

    - by Don Quixote
    I've been coming up to speed on the Haiku operating system, an Open Source clone of BeOS 5 Pro. I'm using an Apple MacBook Pro as my development machine. Apple's BootCamp BIOS does not support more than four partitions on the internal hard drive. While I can set up extended and logical partitions, doing so will prevent any of the installed operating systems from booting. To run Haiku directly on the iron, I boot it off a USB stick. Using external storage is also helpful because I am perpetually out of filesystem space. While VirtualBox is documented to allow access to physical drives, I could not actually get it to work. Also VirtualBox can only use one of the host CPU's cores. While VB guests can be configured for more than one CPU, they are only emulated. A full build of the Haiku OS takes 4.5 under VB. I had the hope of reducing build times by using KVM instead, but it's not working nearly as well as VirtualBox did. The Linux Kernel Virtual Machine is broken in all manner of fundamental ways as seen from Haiku. But I'm a coder; maybe I could contribute to fixing some of those problems. The first problem I've got is that Haiku's video in virt-manager is quite painfully slow. When I drag Haiku windows around the desktop, they lag quite far behind where my mouse is. It's quite difficult to move a window to a precise position on the screen. Just imagine that the mouse was connected to the window title bar with a really stretchy spring. Also Haiku's mouse lags quite far behind where I have moved it. I found lots of Personal Package Archives that enable Spice from QEMU / KVM at the Ubuntu Personal Package Arhives. I tried a few of the PPAs but none of them worked; with one of them, the command "add-apt-repository" crashed with a traceback. There is a Wiki page about Spice, but it says that it only works on 64-bit. My Early 2006 MacBook Pro is 32-bit. Its Apple Model Identifier is MacBookPro1,1; these use Core Duos NOT Core 2 Duos. I don't mind building a source deb for 32-bit if I can expect it to work. Is there some reason that Spice should be 64-bit only? Does it need features of the x86_64 Instruction Set Architecture that x86 does not have? When I try using SDL from virt-manager, the configuration for Local SDL Window says "Xauth: /home/mike/.Xauthority". When I try to start my guest, virt-manager emits an error. When I Googled the error message, the usual solution was to make ~/.Xauthority readible. However, .Xauthorty does not exist in my home directory. Instead I have a $XAUTHORITY environment variable. There is no way to configure SDL in virt-manager to use $XAUTHORITY instead of ~/.Xauthority. Neither does it work to copy the value of $XAUTHORITY into the file. I am ready to scream, because I've been five fscking days trying to make KVM work for Haiku development. There is a whole lot more that is broken than the slow video. All I really want to do for now is speed up my full builds of Haiku by using "jam -j2" to use both cores in my CPU. I may try Xen next, but the last time I monkeyed with Xen it was far, far more broken than I am finding KVM to be. Just for now, I would be satisfied if there were some way to use my USB stick as a drive in VirtualBox. VB does allow me to configure /dev/sdb as a drive, but it always causes a fatal error when I try to launch the guest. Thank You For Any Advice You Can Give Me. -

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  • Using Appendbuffers in unity for terrain generation

    - by Wardy
    Like many others I figured I would try and make the most of the monster processing power of the GPU but I'm having trouble getting the basics in place. CPU code: using UnityEngine; using System.Collections; public class Test : MonoBehaviour { public ComputeShader Generator; public MeshTopology Topology; void OnEnable() { var computedMeshPoints = ComputeMesh(); CreateMeshFrom(computedMeshPoints); } private Vector3[] ComputeMesh() { var size = (32*32) * 4; // 4 points added for each x,z pos var buffer = new ComputeBuffer(size, 12, ComputeBufferType.Append); Generator.SetBuffer(0, "vertexBuffer", buffer); Generator.Dispatch(0, 1, 1, 1); var results = new Vector3[size]; buffer.GetData(results); buffer.Dispose(); return results; } private void CreateMeshFrom(Vector3[] generatedPoints) { var filter = GetComponent<MeshFilter>(); var renderer = GetComponent<MeshRenderer>(); if (generatedPoints.Length > 0) { var mesh = new Mesh { vertices = generatedPoints }; var colors = new Color[generatedPoints.Length]; var indices = new int[generatedPoints.Length]; //TODO: build this different based on topology of the mesh being generated for (int i = 0; i < indices.Length; i++) { indices[i] = i; colors[i] = Color.blue; } mesh.SetIndices(indices, Topology, 0); mesh.colors = colors; mesh.RecalculateNormals(); mesh.Optimize(); mesh.RecalculateBounds(); filter.sharedMesh = mesh; } else { filter.sharedMesh = null; } } } GPU code: #pragma kernel Generate AppendStructuredBuffer<float3> vertexBuffer : register(u0); void genVertsAt(uint2 xzPos) { //TODO: put some height generation code here. // could even run marching cubes / dual contouring code. float3 corner1 = float3( xzPos[0], 0, xzPos[1] ); float3 corner2 = float3( xzPos[0] + 1, 0, xzPos[1] ); float3 corner3 = float3( xzPos[0], 0, xzPos[1] + 1); float3 corner4 = float3( xzPos[0] + 1, 0, xzPos[1] + 1 ); vertexBuffer.Append(corner1); vertexBuffer.Append(corner2); vertexBuffer.Append(corner3); vertexBuffer.Append(corner4); } [numthreads(32, 1, 32)] void Generate (uint3 threadId : SV_GroupThreadID, uint3 groupId : SV_GroupID) { uint2 currentXZ = unint2( groupId.x * 32 + threadId.x, groupId.z * 32 + threadId.z); genVertsAt(currentXZ); } Can anyone explain why when I call "buffer.GetData(results);" on the CPU after the compute dispatch call my buffer is full of Vector3(0,0,0), I'm not expecting any y values yet but I would expect a bunch of thread indexes in the x,z values for the Vector3 array. I'm not getting any errors in any of this code which suggests it's correct syntax-wise but maybe the issue is a logical bug. Also: Yes, I know I'm generating 4,000 Vector3's and then basically round tripping them. However, the purpose of this code is purely to learn how round tripping works between CPU and GPU in Unity.

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Fast Data - Big Data's achilles heel

    - by thegreeneman
    At OOW 2013 in Mark Hurd and Thomas Kurian's keynote, they discussed Oracle's Fast Data software solution stack and discussed a number of customers deploying Oracle's Big Data / Fast Data solutions and in particular Oracle's NoSQL Database.  Since that time, there have been a large number of request seeking clarification on how the Fast Data software stack works together to deliver on the promise of real-time Big Data solutions.   Fast Data is a software solution stack that deals with one aspect of Big Data, high velocity.   The software in the Fast Data solution stack involves 3 key pieces and their integration:  Oracle Event Processing, Oracle Coherence, Oracle NoSQL Database.   All three of these technologies address a high throughput, low latency data management requirement.   Oracle Event Processing enables continuous query to filter the Big Data fire hose, enable intelligent chained events to real-time service invocation and augments the data stream to provide Big Data enrichment. Extended SQL syntax allows the definition of sliding windows of time to allow SQL statements to look for triggers on events like breach of weighted moving average on a real-time data stream.    Oracle Coherence is a distributed, grid caching solution which is used to provide very low latency access to cached data when the data is too big to fit into a single process, so it is spread around in a grid architecture to provide memory latency speed access.  It also has some special capabilities to deploy remote behavioral execution for "near data" processing.   The Oracle NoSQL Database is designed to ingest simple key-value data at a controlled throughput rate while providing data redundancy in a cluster to facilitate highly concurrent low latency reads.  For example, when large sensor networks are generating data that need to be captured while analysts are simultaneously extracting the data using range based queries for upstream analytics.  Another example might be storing cookies from user web sessions for ultra low latency user profile management, also leveraging that data using holistic MapReduce operations with your Hadoop cluster to do segmented site analysis.  Understand how NoSQL plays a critical role in Big Data capture and enrichment while simultaneously providing a low latency and scalable data management infrastructure thru clustered, always on, parallel processing in a shared nothing architecture. Learn how easily a NoSQL cluster can be deployed to provide essential services in industry specific Fast Data solutions. See these technologies work together in a demonstration highlighting the salient features of these Fast Data enabling technologies in a location based personalization service. The question then becomes how do these things work together to deliver an end to end Fast Data solution.  The answer is that while different applications will exhibit unique requirements that may drive the need for one or the other of these technologies, often when it comes to Big Data you may need to use them together.   You may have the need for the memory latencies of the Coherence cache, but just have too much data to cache, so you use a combination of Coherence and Oracle NoSQL to handle extreme speed cache overflow and retrieval.   Here is a great reference to how these two technologies are integrated and work together.  Coherence & Oracle NoSQL Database.   On the stream processing side, it is similar as with the Coherence case.  As your sliding windows get larger, holding all the data in the stream can become difficult and out of band data may need to be offloaded into persistent storage.  OEP needs an extreme speed database like Oracle NoSQL Database to help it continue to perform for the real time loop while dealing with persistent spill in the data stream.  Here is a great resource to learn more about how OEP and Oracle NoSQL Database are integrated and work together.  OEP & Oracle NoSQL Database.

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  • The best computer ever

    - by Jeff
    (This is a repost from my personal blog… wow… I need to write more technical stuff!) About three years and three months ago, I bought a 17" MacBook Pro, and it turned out to be the best computer I've ever owned. You might think that every computer with better specs is automatically better than the last, but that hasn't been my experience. My first one was a Sony, back in the Pentium III days, and it cost an astonishing $2,500. That was even more ridiculous in 1999 dollars. It had a dial-up modem, and a CD-ROM, built-in! It may have even played DVD's. A few years later I bought an HP, and it ended up being a pile of shit. The power connector inside came loose from the board, and on occasion would even short. In 2005, I bought a Dell, and it wasn't bad. It had a really high resolution screen (complete with dead pixels, a problem in those days), and it was the first laptop I felt I could do real work on. When 2006 rolled around, Apple started making computers with Intel CPU's, and I bought the very first one the week it came out. I used Boot Camp to run Windows. I still have it in its box somewhere, and I used it for three years. The current 17" was new in 2009. The goodness was largely rooted in having a big screen with lots of dots. This computer has been the source of hundreds of blog posts, tens of thousands of lines of code, video and photo editing, and of course, a whole lot of Web surfing. It connected to corpnet at Microsoft, WiFi in Hawaii and has presented many a deck. It has traveled with me tens of thousands of miles. Last year, I put a solid state drive in it, and it was like getting a new computer. I can boot up a Windows 7 VM in about 19 seconds. Having 8 gigs of RAM has always been fantastic. Everything about it has been fast and fun. When new, the battery (when not using VM's) could get as much as 10 hours. I can still do 7 without much trouble. After 460 charge cycles, the battery health is still between 85 and 90%. The only real negative has been the size and weight. It's only an inch thick, but naturally it's pretty big with a 17" screen. You don't get battery life like that without a huge battery, either, so it's heavy. It was never a deal breaker, but sometimes a long haul across a large airport, you know you're carrying it. Today, Apple announced a new, thinner and lighter 15" laptop, with twice the RAM and CPU cores, and four times the screen resolution. It basically handles my size and weight issues while retaining the resolution, and it still costs less than my 17" did. So I ordered one. Three years is an excellent run, but I kind of budgeted for a new workhorse this year anyway. So if you're interested in a 17" MacBook Pro with a Core 2 Duo 2.66 GHz CPU, 8 gigs of RAM and a 320 gig hard drive (sorry, I'm keeping the SSD), I have one to sell. They've apparently discontinued the 17", which is going to piss off the video community. It's in excellent condition, with a few minor scratches, but I take care of my stuff.

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  • Faster Memory Allocation Using vmtasks

    - by Steve Sistare
    You may have noticed a new system process called "vmtasks" on Solaris 11 systems: % pgrep vmtasks 8 % prstat -p 8 PID USERNAME SIZE RSS STATE PRI NICE TIME CPU PROCESS/NLWP 8 root 0K 0K sleep 99 -20 9:10:59 0.0% vmtasks/32 What is vmtasks, and why should you care? In a nutshell, vmtasks accelerates creation, locking, and destruction of pages in shared memory segments. This is particularly helpful for locked memory, as creating a page of physical memory is much more expensive than creating a page of virtual memory. For example, an ISM segment (shmflag & SHM_SHARE_MMU) is locked in memory on the first shmat() call, and a DISM segment (shmflg & SHM_PAGEABLE) is locked using mlock() or memcntl(). Segment operations such as creation and locking are typically single threaded, performed by the thread making the system call. In many applications, the size of a shared memory segment is a large fraction of total physical memory, and the single-threaded initialization is a scalability bottleneck which increases application startup time. To break the bottleneck, we apply parallel processing, harnessing the power of the additional CPUs that are always present on modern platforms. For sufficiently large segments, as many of 16 threads of vmtasks are employed to assist an application thread during creation, locking, and destruction operations. The segment is implicitly divided at page boundaries, and each thread is given a chunk of pages to process. The per-page processing time can vary, so for dynamic load balancing, the number of chunks is greater than the number of threads, and threads grab chunks dynamically as they finish their work. Because the threads modify a single application address space in compressed time interval, contention on locks protecting VM data structures locks was a problem, and we had to re-scale a number of VM locks to get good parallel efficiency. The vmtasks process has 1 thread per CPU and may accelerate multiple segment operations simultaneously, but each operation gets at most 16 helper threads to avoid monopolizing CPU resources. We may reconsider this limit in the future. Acceleration using vmtasks is enabled out of the box, with no tuning required, and works for all Solaris platform architectures (SPARC sun4u, SPARC sun4v, x86). The following tables show the time to create + lock + destroy a large segment, normalized as milliseconds per gigabyte, before and after the introduction of vmtasks: ISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1386 245 6X X7560 64 1016 153 7X M9000 512 1196 206 6X T5240 128 2506 234 11X T4-2 128 1197 107 11x DISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1582 265 6X X7560 64 1116 158 7X M9000 512 1165 152 8X T5240 128 2796 198 14X (I am missing the data for T4 DISM, for no good reason; it works fine). The following table separates the creation and destruction times: ISM, T4-2 before after ------ ----- create 702 64 destroy 495 43 To put this in perspective, consider creating a 512 GB ISM segment on T4-2. Creating the segment would take 6 minutes with the old code, and only 33 seconds with the new. If this is your Oracle SGA, you save over 5 minutes when starting the database, and you also save when shutting it down prior to a restart. Those minutes go directly to your bottom line for service availability.

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  • Problem with sprite direction and rotation

    - by user2236165
    I have a sprite called Tool that moves with a speed represented as a float and in a direction represented as a Vector2. When I click the mouse on the screen the sprite change its direction and starts to move towards the mouseclick. In addition to that I rotate the sprite so that it is facing in the direction it is heading. However, when I add a camera that is suppose to follow the sprite so that the sprite is always centered on the screen, the sprite won't move in the given direction and the rotation isn't accurate anymore. This only happens when I add the Camera.View in the spriteBatch.Begin(). I was hoping anyone could maybe shed a light on what I am missing in my code, that would be highly appreciated. Here is the camera class i use: public class Camera { private const float zoomUpperLimit = 1.5f; private const float zoomLowerLimit = 0.1f; private float _zoom; private Vector2 _pos; private int ViewportWidth, ViewportHeight; #region Properties public float Zoom { get { return _zoom; } set { _zoom = value; if (_zoom < zoomLowerLimit) _zoom = zoomLowerLimit; if (_zoom > zoomUpperLimit) _zoom = zoomUpperLimit; } } public Rectangle Viewport { get { int width = (int)((ViewportWidth / _zoom)); int height = (int)((ViewportHeight / _zoom)); return new Rectangle((int)(_pos.X - width / 2), (int)(_pos.Y - height / 2), width, height); } } public void Move(Vector2 amount) { _pos += amount; } public Vector2 Position { get { return _pos; } set { _pos = value; } } public Matrix View { get { return Matrix.CreateTranslation(new Vector3(-_pos.X, -_pos.Y, 0)) * Matrix.CreateScale(new Vector3(Zoom, Zoom, 1)) * Matrix.CreateTranslation(new Vector3(ViewportWidth * 0.5f, ViewportHeight * 0.5f, 0)); } } #endregion public Camera(Viewport viewport, float initialZoom) { _zoom = initialZoom; _pos = Vector2.Zero; ViewportWidth = viewport.Width; ViewportHeight = viewport.Height; } } And here is my Update and Draw-method: protected override void Update (GameTime gameTime) { float elapsed = (float)gameTime.ElapsedGameTime.TotalSeconds; TouchCollection touchCollection = TouchPanel.GetState (); foreach (TouchLocation tl in touchCollection) { if (tl.State == TouchLocationState.Pressed || tl.State == TouchLocationState.Moved) { //direction the tool shall move towards direction = touchCollection [0].Position - toolPos; if (direction != Vector2.Zero) { direction.Normalize (); } //change the direction the tool is moving and find the rotationangle the texture must rotate to point in given direction toolPos += (direction * speed * elapsed); RotationAngle = (float)Math.Atan2 (direction.Y, direction.X); } } if (direction != Vector2.Zero) { direction.Normalize (); } //move tool in given direction toolPos += (direction * speed * elapsed); //change cameracentre to the tools position Camera.Position = toolPos; base.Update (gameTime); } protected override void Draw (GameTime gameTime) { graphics.GraphicsDevice.Clear (Color.Blue); spriteBatch.Begin (SpriteSortMode.BackToFront, BlendState.AlphaBlend, null, null, null, null, Camera.View); spriteBatch.Draw (tool, new Vector2 (toolPos.X, toolPos.Y), null, Color.White, RotationAngle, originOfToolTexture, 1, SpriteEffects.None, 1); spriteBatch.End (); base.Draw (gameTime); }

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  • Using WKA in Large Coherence Clusters (Disabling Multicast)

    - by jpurdy
    Disabling hardware multicast (by configuring well-known addresses aka WKA) will place significant stress on the network. For messages that must be sent to multiple servers, rather than having a server send a single packet to the switch and having the switch broadcast that packet to the rest of the cluster, the server must send a packet to each of the other servers. While hardware varies significantly, consider that a server with a single gigabit connection can send at most ~70,000 packets per second. To continue with some concrete numbers, in a cluster with 500 members, that means that each server can send at most 140 cluster-wide messages per second. And if there are 10 cluster members on each physical machine, that number shrinks to 14 cluster-wide messages per second (or with only mild hyperbole, roughly zero). It is also important to keep in mind that network I/O is not only expensive in terms of the network itself, but also the consumption of CPU required to send (or receive) a message (due to things like copying the packet bytes, processing a interrupt, etc). Fortunately, Coherence is designed to rely primarily on point-to-point messages, but there are some features that are inherently one-to-many: Announcing the arrival or departure of a member Updating partition assignment maps across the cluster Creating or destroying a NamedCache Invalidating a cache entry from a large number of client-side near caches Distributing a filter-based request across the full set of cache servers (e.g. queries, aggregators and entry processors) Invoking clear() on a NamedCache The first few of these are operations that are primarily routed through a single senior member, and also occur infrequently, so they usually are not a primary consideration. There are cases, however, where the load from introducing new members can be substantial (to the point of destabilizing the cluster). Consider the case where cluster in the first paragraph grows from 500 members to 1000 members (holding the number of physical machines constant). During this period, there will be 500 new member introductions, each of which may consist of several cluster-wide operations (for the cluster membership itself as well as the partitioned cache services, replicated cache services, invocation services, management services, etc). Note that all of these introductions will route through that one senior member, which is sharing its network bandwidth with several other members (which will be communicating to a lesser degree with other members throughout this process). While each service may have a distinct senior member, there's a good chance during initial startup that a single member will be the senior for all services (if those services start on the senior before the second member joins the cluster). It's obvious that this could cause CPU and/or network starvation. In the current release of Coherence (3.7.1.3 as of this writing), the pure unicast code path also has less sophisticated flow-control for cluster-wide messages (compared to the multicast-enabled code path), which may also result in significant heap consumption on the senior member's JVM (from the message backlog). This is almost never a problem in practice, but with sufficient CPU or network starvation, it could become critical. For the non-operational concerns (near caches, queries, etc), the application itself will determine how much load is placed on the cluster. Applications intended for deployment in a pure unicast environment should be careful to avoid excessive dependence on these features. Even in an environment with multicast support, these operations may scale poorly since even with a constant request rate, the underlying workload will increase at roughly the same rate as the underlying resources are added. Unless there is an infrastructural requirement to the contrary, multicast should be enabled. If it can't be enabled, care should be taken to ensure the added overhead doesn't lead to performance or stability issues. This is particularly crucial in large clusters.

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  • installer can't find partition, but fdisk can find them

    - by pxd
    I'm installing ubuntu 12.04, my system had install 2 system -- winxp and ubuntu 10.10. Now, I want to update ubuntu to 12.04. I use usb disk to install 12.04. But, the installer can't not find my partition in my harddisk. But, the fdisk can find them. Can you help me? How to do? ubuntu@ubuntu:~$ sudo lshw -short H/W path Device Class Description system HP 2230s (NN868PA#AB2) /0 bus 3037 /0/9 memory 64KiB BIOS /0/0 processor Intel(R) Core(TM)2 Duo CPU T6570 @ 2.10GHz /0/0/1 memory 2MiB L2 cache /0/0/3 memory 32KiB L1 cache /0/0/0.1 processor Logical CPU /0/0/0.2 processor Logical CPU /0/2 memory 32KiB L1 cache /0/4 memory 2GiB System Memory /0/4/0 memory SODIMM [empty] /0/4/1 memory 2GiB SODIMM DDR2 Synchronous 800 MHz (1.2 ns) /0/100 bridge Mobile 4 Series Chipset Memory Controller Hub /0/100/2 display Mobile 4 Series Chipset Integrated Graphics Controller /0/100/2.1 display Mobile 4 Series Chipset Integrated Graphics Controller /0/100/1a bus 82801I (ICH9 Family) USB UHCI Controller #4 /0/100/1a.1 bus 82801I (ICH9 Family) USB UHCI Controller #5 /0/100/1a.2 bus 82801I (ICH9 Family) USB UHCI Controller #6 /0/100/1a.7 bus 82801I (ICH9 Family) USB2 EHCI Controller #2 /0/100/1b multimedia 82801I (ICH9 Family) HD Audio Controller /0/100/1c bridge 82801I (ICH9 Family) PCI Express Port 1 /0/100/1c.1 bridge 82801I (ICH9 Family) PCI Express Port 2 /0/100/1c.1/0 wlan1 network PRO/Wireless 5100 AGN [Shiloh] Network Connection /0/100/1c.2 bridge 82801I (ICH9 Family) PCI Express Port 3 /0/100/1c.4 bridge 82801I (ICH9 Family) PCI Express Port 5 /0/100/1c.5 bridge 82801I (ICH9 Family) PCI Express Port 6 /0/100/1c.5/0 eth1 network 88E8072 PCI-E Gigabit Ethernet Controller /0/100/1d bus 82801I (ICH9 Family) USB UHCI Controller #1 /0/100/1d.1 bus 82801I (ICH9 Family) USB UHCI Controller #2 /0/100/1d.2 bus 82801I (ICH9 Family) USB UHCI Controller #3 /0/100/1d.7 bus 82801I (ICH9 Family) USB2 EHCI Controller #1 /0/100/1e bridge 82801 Mobile PCI Bridge /0/100/1f bridge ICH9M LPC Interface Controller /0/100/1f.2 scsi0 storage 82801IBM/IEM (ICH9M/ICH9M-E) 4 port SATA Controller [AHCI mode] /0/100/1f.2/0 /dev/sda disk 500GB WDC WD5000BEVT-0 /0/100/1f.2/0/1 /dev/sda1 volume 48GiB Windows NTFS volume /0/100/1f.2/0/2 /dev/sda2 volume 416GiB Extended partition /0/100/1f.2/0/2/5 /dev/sda5 volume 97GiB HPFS/NTFS partition /0/100/1f.2/0/2/6 /dev/sda6 volume 198GiB HPFS/NTFS partition /0/100/1f.2/0/2/7 /dev/sda7 volume 27GiB Linux filesystem partition /0/100/1f.2/0/2/8 /dev/sda8 volume 93GiB Linux filesystem partition /0/100/1f.2/1 /dev/cdrom disk CDDVDW TS-L633M /0/1 scsi6 storage /0/1/0.0.0 /dev/sdb disk 15GB STORAGE DEVICE /0/1/0.0.0/0 /dev/sdb disk 15GB /0/1/0.0.0/0/1 /dev/sdb1 volume 14GiB Windows FAT volume /1 power HZ04037 ubuntu@ubuntu:~$ ubuntu@ubuntu:~$ sudo fdisk -l Disk /dev/sda: 500.1 GB, 500107862016 bytes 255 heads, 63 sectors/track, 60801 cylinders, total 976773168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x31263125 Device Boot Start End Blocks Id System /dev/sda1 * 63 102277727 51138832+ 7 HPFS/NTFS/exFAT /dev/sda2 102277728 976784129 437253201 f W95 Ext'd (LBA) /dev/sda5 102277791 307078127 102400168+ 7 HPFS/NTFS/exFAT /dev/sda6 307078191 724141151 208531480+ 7 HPFS/NTFS/exFAT /dev/sda7 724142080 781459455 28658688 83 Linux /dev/sda8 781461504 976771071 97654784 83 Linux Disk /dev/sdb: 15.9 GB, 15931539456 bytes 64 heads, 32 sectors/track, 15193 cylinders, total 31116288 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x0009eb92 Device Boot Start End Blocks Id Systemfile:///home/ubuntu/Pictures/Screenshot%20from%202012-07-07%2010:25:40.png /dev/sdb1 * 32 31115263 15557616 c W95 FAT32 (LBA) ubuntu 12.04 installer can't find the partition in my hard disk, only find device - /dev/sda.(sorry, I'm new user, so can't send image.)

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  • High Resolution Timeouts

    - by user12607257
    The default resolution of application timers and timeouts is now 1 msec in Solaris 11.1, down from 10 msec in previous releases. This improves out-of-the-box performance of polling and event based applications, such as ticker applications, and even the Oracle rdbms log writer. More on that in a moment. As a simple example, the poll() system call takes a timeout argument in units of msec: System Calls poll(2) NAME poll - input/output multiplexing SYNOPSIS int poll(struct pollfd fds[], nfds_t nfds, int timeout); In Solaris 11, a call to poll(NULL,0,1) returns in 10 msec, because even though a 1 msec interval is requested, the implementation rounds to the system clock resolution of 10 msec. In Solaris 11.1, this call returns in 1 msec. In specification lawyer terms, the resolution of CLOCK_REALTIME, introduced by POSIX.1b real time extensions, is now 1 msec. The function clock_getres(CLOCK_REALTIME,&res) returns 1 msec, and any library calls whose man page explicitly mention CLOCK_REALTIME, such as nanosleep(), are subject to the new resolution. Additionally, many legacy functions that pre-date POSIX.1b and do not explicitly mention a clock domain, such as poll(), are subject to the new resolution. Here is a fairly comprehensive list: nanosleep pthread_mutex_timedlock pthread_mutex_reltimedlock_np pthread_rwlock_timedrdlock pthread_rwlock_reltimedrdlock_np pthread_rwlock_timedwrlock pthread_rwlock_reltimedwrlock_np mq_timedreceive mq_reltimedreceive_np mq_timedsend mq_reltimedsend_np sem_timedwait sem_reltimedwait_np poll select pselect _lwp_cond_timedwait _lwp_cond_reltimedwait semtimedop sigtimedwait aiowait aio_waitn aio_suspend port_get port_getn cond_timedwait cond_reltimedwait setitimer (ITIMER_REAL) misc rpc calls, misc ldap calls This change in resolution was made feasible because we made the implementation of timeouts more efficient a few years back when we re-architected the callout subsystem of Solaris. Previously, timeouts were tested and expired by the kernel's clock thread which ran 100 times per second, yielding a resolution of 10 msec. This did not scale, as timeouts could be posted by every CPU, but were expired by only a single thread. The resolution could be changed by setting hires_tick=1 in /etc/system, but this caused the clock thread to run at 1000 Hz, which made the potential scalability problem worse. Given enough CPUs posting enough timeouts, the clock thread could be a performance bottleneck. We fixed that by re-implementing the timeout as a per-CPU timer interrupt (using the cyclic subsystem, for those familiar with Solaris internals). This decoupled the clock thread frequency from timeout resolution, and allowed us to improve default timeout resolution without adding CPU overhead in the clock thread. Here are some exceptions for which the default resolution is still 10 msec. The thread scheduler's time quantum is 10 msec by default, because preemption is driven by the clock thread (plus helper threads for scalability). See for example dispadmin, priocntl, fx_dptbl, rt_dptbl, and ts_dptbl. This may be changed using hires_tick. The resolution of the clock_t data type, primarily used in DDI functions, is 10 msec. It may be changed using hires_tick. These functions are only used by developers writing kernel modules. A few functions that pre-date POSIX CLOCK_REALTIME mention _SC_CLK_TCK, CLK_TCK, "system clock", or no clock domain. These functions are still driven by the clock thread, and their resolution is 10 msec. They include alarm, pcsample, times, clock, and setitimer for ITIMER_VIRTUAL and ITIMER_PROF. Their resolution may be changed using hires_tick. Now back to the database. How does this help the Oracle log writer? Foreground processes post a redo record to the log writer, which releases them after the redo has committed. When a large number of foregrounds are waiting, the release step can slow down the log writer, so under heavy load, the foregrounds switch to a mode where they poll for completion. This scales better because every foreground can poll independently, but at the cost of waiting the minimum polling interval. That was 10 msec, but is now 1 msec in Solaris 11.1, so the foregrounds process transactions faster under load. Pretty cool.

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  • ?SPARC T4?????????????·???? : Netra SPARC T4-1

    - by user13138700
    ?SPARC T4???????????????·??????? Netra SPARC T4-1 ???? Netra SPARC T4-2 ?2012?1?10??????????3?15??????????????(????) ?????????? Netra SPARC T4-1 ? 4core ???( T4 ???????? 4core ???)(*)???????????????????????????(*)( Netra SPARC T4-1 ?????? 4core ???? 8core ????????) ??? prtdiag ????? pginfo ??????????????? 8????/1core ???? prtdiag ????????4core=32???????????????pginfo ?????????????????core ???????????????????? # ./prtdiag -v System Configuration: Oracle Corporation sun4v Netra SPARC T4-1 ???????: 130560 M ??? ================================ ?? CPU ================================ CPU ID Frequency Implementation Status ------ --------- ---------------------- ------- 0 2848 MHz SPARC-T4 on-line 1 2848 MHz SPARC-T4 on-line 2 2848 MHz SPARC-T4 on-line 3 2848 MHz SPARC-T4 on-line 4 2848 MHz SPARC-T4 on-line 5 2848 MHz SPARC-T4 on-line 6 2848 MHz SPARC-T4 on-line 7 2848 MHz SPARC-T4 on-line 8 2848 MHz SPARC-T4 on-line 9 2848 MHz SPARC-T4 on-line 10 2848 MHz SPARC-T4 on-line 11 2848 MHz SPARC-T4 on-line 12 2848 MHz SPARC-T4 on-line 13 2848 MHz SPARC-T4 on-line 14 2848 MHz SPARC-T4 on-line 15 2848 MHz SPARC-T4 on-line 16 2848 MHz SPARC-T4 on-line 17 2848 MHz SPARC-T4 on-line 18 2848 MHz SPARC-T4 on-line 19 2848 MHz SPARC-T4 on-line 20 2848 MHz SPARC-T4 on-line 21 2848 MHz SPARC-T4 on-line 22 2848 MHz SPARC-T4 on-line 23 2848 MHz SPARC-T4 on-line 24 2848 MHz SPARC-T4 on-line 25 2848 MHz SPARC-T4 on-line 26 2848 MHz SPARC-T4 on-line 27 2848 MHz SPARC-T4 on-line 28 2848 MHz SPARC-T4 on-line 29 2848 MHz SPARC-T4 on-line 30 2848 MHz SPARC-T4 on-line 31 2848 MHz SPARC-T4 on-line ======================= Physical Memory Configuration ======================== ???? # pginfo -p -T 0 (System [system,chip]) CPUs: 0-31 `-- 3 (Data_Pipe_to_memory [system,chip]) CPUs: 0-31 |-- 2 (Floating_Point_Unit [core]) CPUs: 0-7 | `-- 1 (Integer_Pipeline [core]) CPUs: 0-7 |-- 5 (Floating_Point_Unit [core]) CPUs: 8-15 | `-- 4 (Integer_Pipeline [core]) CPUs: 8-15 |-- 7 (Floating_Point_Unit [core]) CPUs: 16-23 | `-- 6 (Integer_Pipeline [core]) CPUs: 16-23 `-- 9 (Floating_Point_Unit [core]) CPUs: 24-31 `-- 8 (Integer_Pipeline [core]) CPUs: 24-31 T4 ????????????????????????????????????????????????? T3 ?????(S2 core)?????T4 ?????(S3 core)?????????????5???????????? T3 ?????(S2 core)?????????????????????????(????????)?????????????????????????????????????????????·???????????????????????????????????????? ????T4 ????????????????????????????T4 ??????????·??????? Netra SPARC T4-1 4core ????????????????????????????????????T3 ???????????????????????????? ?????????Netra SPARC T4-1 ??????????????? Netra SPARC T4-1 ?? Computing 1 x SPARC T4 4?? 32???? or 8 ?? 64 ???? 2.85GHz CPU (1?????8????) 16 x DDR3 DIMM (?? 256GB ?????16GB DIMM ???) I/O and Storage 3 x Low Profile PCI-Express Gen2 ???? (2 x 10Gb Ethernet XAUI ???????) 2 x Full-height Half-length PCI-Express Gen2 ???? 4 x 10/100/1000 Ethernet ???????? 4 x 2.5” SAS2 HDD 4 x USB ??? (?? 2, ?? 2) RAS and Management and Power Supply ???? (RAID????), ????PSU ?????????? ILOM?????????????? 2N (1+1) , AC ???? DC ?? Support OS Oracle Solaris 10 10/9, 9/10, 8/11, Oracle Solaris 11 11/11 Oracle VM Server for SPARC 2.1 (LDoms) ???? ??? NEBS Level3?? ??????21” 19”(EIA-310D),23”,24”,600mm????? ?????(?????)????????? ????SPARC T4 ????????SPARC T4 ?????????????????????????(4???)???????????? Oracle OpenWorld Tokyo 2012 ?3??(4/4(?)?4/5(?)?4/6(?))?????????????????????&?????????????????SPARC T4 ?????????????????????????????????·?????????????????SPARC T4 ???????????????????!? Oracle OpenWorld Tokyo 2012 http://www.oracle.com/openworld/jp-ja/index.html ????·???????????? 4/6(?) Develop D3-13 (14:00 - 14:45) ???????????49 ??? ?????? 7264 ???????????????

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  • MSAcpi_ThermalZoneTemperature class not showing actual temperature

    - by jchoudhury
    i want to fetch CPU Performance data in real time including temperature. i used the following code to get CPU Temperature: try { ManagementObjectSearcher searcher = new ManagementObjectSearcher("root\\WMI", "SELECT * FROM MSAcpi_ThermalZoneTemperature"); foreach (ManagementObject queryObj in searcher.Get()) { double temp = Convert.ToDouble(queryObj["CurrentTemperature"].ToString()); double temp_critical = Convert.ToDouble(queryObj["CriticalTripPoint"].ToString()); double temp_cel = (temp/10 - 273.15); double temp_critical_cel = temp_critical / 10 - 273.15; lblCurrentTemp.Text = temp_cel.ToString(); lblCriticalTemp.Text = temp_critical_cel.ToString(); } } catch (ManagementException e) { MessageBox.Show("An error occurred while querying for WMI data: " + e.Message); } but this code shows the temperature that is not the correct temperature. It ususally shows 49.5-50.5 degrees centigrade. But I used "OpenHardwareMonitor" that report CPU temperature over 71 degree centigrade and changing fractions along with time fractions. is there anything I am missing in the code? I used the above code in timer_click event for every 500ms interval to refresh the temperature reading but it's always showing the same temperature from the beginning of execution. That implies if you run this application and if it shows 49 degree then after 1 hour session, it'll constantly show 49 degree. Where is the problem? please help. Thanks in advance.

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  • Context migration in CUDA.NET

    - by Vyacheslav
    I'm currently using CUDA.NET library by GASS. I need to initialize cuda arrays (actually cublas vectors, but it doesn't matters) in one CPU thread and use them in other CPU thread. But CUDA context which holding all initialized arrays and loaded functions, can be attached to only one CPU thread. There is mechanism called context migration API to detach context from one thread and attach it to another. But i don't how to properly use it in CUDA.NET. I tried something like this: class Program { private static float[] vector1, vector2; private static CUDA cuda; private static CUBLAS cublas; private static CUdeviceptr ptr; static void Main(string[] args) { cuda = new CUDA(false); cublas = new CUBLAS(cuda); cuda.Init(); cuda.CreateContext(0); AllocateVectors(); cuda.DetachContext(); CUcontext context = cuda.PopCurrentContext(); GetVectorFromDeviceAsync(context); } private static void AllocateVectors() { vector1 = new float[]{1f, 2f, 3f, 4f, 5f}; ptr = cublas.Allocate(vector1.Length, sizeof (float)); cublas.SetVector(vector1, ptr); vector2 = new float[5]; } private static void GetVectorFromDevice(object objContext) { CUcontext localContext = (CUcontext) objContext; cuda.PushCurrentContext(localContext); cuda.AttachContext(localContext); //change vector somehow vector1[0] = -1; //copy changed vector to device cublas.SetVector(vector1, ptr); cublas.GetVector(ptr, vector2); CUDADriver.cuCtxPopCurrent(ref localContext); } private static void GetVectorFromDeviceAsync(CUcontext cUcontext) { Thread thread = new Thread(GetVectorFromDevice); thread.IsBackground = false; thread.Start(cUcontext); } } But execution fails on attempt to copy changed vector to device because context is not attached? Any ideas how i can get it work?

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  • How to get Processor and Motherboard Id ?

    - by Frank
    I use the code from http://www.rgagnon.com/javadetails/java-0580.html to get Motherboard Id, but the result is "null", <1 How can that be ? <2 Also I modified the code a bit to look like this to get processor Id : "Set objWMIService = GetObject(\"winmgmts:\\\\.\\root\\cimv2\")\n"+ "Set colItems = objWMIService.ExecQuery _ \n"+ " (\"Select * from Win32_Processor\") \n"+ "For Each objItem in colItems \n"+ " Wscript.Echo objItem.ProcessorId \n"+ " exit for ' do the first cpu only! \n"+ "Next \n"; The result is something like : ProcessorId = BFEBFBFF00010676 On http://msdn.microsoft.com/en-us/library/aa389273%28VS.85%29.aspx it says : ProcessorId : Processor information that describes the processor features. For an x86 class CPU, the field format depends on the processor support of the CPUID instruction. If the instruction is supported, the property contains 2 (two) DWORD formatted values. The first is an offset of 08h-0Bh, which is the EAX value that a CPUID instruction returns with input EAX set to 1. The second is an offset of 0Ch-0Fh, which is the EDX value that the instruction returns. Only the first two bytes of the property are significant and contain the contents of the DX register at CPU reset—all others are set to 0 (zero), and the contents are in DWORD format. I don't quite understand it, in plain English, is it unique or just a number for this class of processors, for instance all Intel Core2 Duo P8400 will have this number ? Frank

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  • Single player 'pong' game

    - by Jam
    I am just starting out learning pygame and livewires, and I'm trying to make a single-player pong game, where you just hit the ball, and it bounces around until it passes your paddle (located on the left side of the screen and controlled by the mouse), which makes you lose. I have the basic code, but the ball doesn't stay on the screen, it just flickers and doesn't remain constant. Also, the paddle does not move with the mouse. I'm sure I'm missing something simple, but I just can't figure it out. Help please! Here's what I have: from livewires import games import random games.init(screen_width=640, screen_height=480, fps=50) class Paddle(games.Sprite): image=games.load_image("paddle.bmp") def __init__(self, x=10): super(Paddle, self).__init__(image=Paddle.image, y=games.mouse.y, left=10) self.score=games.Text(value=0, size=25, top=5, right=games.screen.width - 10) games.screen.add(self.score) def update(self): self.y=games.mouse.y if self.top<0: self.top=0 if self.bottom>games.screen.height: self.bottom=games.screen.height self.check_collide() def check_collide(self): for ball in self.overlapping_sprites: self.score.value+=1 ball.handle_collide() class Ball(games.Sprite): image=games.load_image("ball.bmp") speed=5 def __init__(self, x=90, y=90): super(Ball, self).__init__(image=Ball.image, x=x, y=y, dx=Ball.speed, dy=Ball.speed) def update(self): if self.right>games.screen.width: self.dx=-self.dx if self.bottom>games.screen.height or self.top<0: self.dy=-self.dy if self.left<0: self.end_game() self.destroy() def handle_collide(self): self.dx=-self.dx def end_game(self): end_message=games.Message(value="Game Over", size=90, x=games.screen.width/2, y=games.screen.height/2, lifetime=250, after_death=games.screen.quit) games.screen.add(end_message) def main(): background_image=games.load_image("background.bmp", transparent=False) games.screen.background=background_image paddle_image=games.load_image("paddle.bmp") the_paddle=games.Sprite(image=paddle_image, x=10, y=games.mouse.y) games.screen.add(the_paddle) ball_image=games.load_image("ball.bmp") the_ball=games.Sprite(image=ball_image, x=630, y=200, dx=2, dy=2) games.screen.add(the_ball) games.mouse.is_visible=False games.screen.event_grab=True games.screen.mainloop() main()

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  • Single-player pong game

    - by Jam
    I am just starting out learning pygame and livewires, and I'm trying to make a single-player pong game, where you just hit the ball, and it bounces around until it passes your paddle (located on the left side of the screen and controlled by the mouse), which makes you lose. However, I keep getting the error: "Cannot have more than on Screen object", which I can find no references to online really, and I can't make sense of it. I want to eventually make the game more complicated, but I need to make it work first. Help please! Here's the code so far: from livewires import games games.init(screen_width=640, screen_height=480, fps=50) class Paddle(games.Sprite): image=games.load_image("paddle.bmp") def __init__(self): super(Paddle, self).__init__(image=Paddle.image, y=games.mouse.y, left=0) self.score=games.Text(value=0, size=25, top=5, right=games.screen.width-10) games.screen.add(self.score) def update(self): self.y=games.mouse.y self.check_collide() def check_collide(self): for ball in self.overlapping_sprites: self.score.value+=1 self.score.right=games.screen.width-10 ball.handle_collide() class Ball(games.Sprite): image=games.load_image("ball.bmp") speed=1 def __init__(self, x, y=90): super(Ball, self).__init__(image=Ball.image, x=x, y=y, dx=Ball.speed, dy=Ball.speed) def update(self): if self.left<0: self.end_game() self.destroy() def handle_collide(self): if self.right>games.screen.width: self.dx=-self.dx if self.bottom>games.screen.height or self.top<0: self.dy=-self.dy def ball_destroy(self): self.destroy() def main(): background_image=games.load_image("background.bmp", transparent=False) games.screen.background=background_image the_ball=Ball() games.screen.add(the_ball) the_paddle=Paddle() games.screen.add(the_paddle) games.mouse.is_visible=False games.screen.event_grab=True games.screen.mainloop() main()

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  • Surprising results with .NET multi-theading algorithm

    - by Myles J
    Hi, I've recently wrote a C# console time tabling algorithm that is based on a combination of a genetic algorithm with a few brute force routines thrown in. The initial results were promising but I figured I could improve the performance by splitting the brute force routines up to run in parallel on multi processor architectures. To do this I used the well documented Producer/Consumer model (as documented in this fantastic article http://www.albahari.com/threading/part2.aspx#_ProducerConsumerQWaitHandle). I changed my code to create one thread per logical processor during the brute force routines. The performance gains on my work station were very pleasing. I am running Windows XP on the following hardware: Intel Core 2 Quad CPU 2.33 GHz 3.49 GB RAM Initial tests indicated average performance gains of approx 40% when using 4 threads. The next step was to deploy the new multi-threading version of the algorithm to our higher spec UAT server. Here is the spec of our UAT server: Windows 2003 Server R2 Enterprise x64 8 cpu (Quad-Core) AMD Opteron 2.70 GHz 255 GB RAM After running the first round of tests we were all extremely surprised to find that the algorithm actually runs slower on the high spec W2003 server than on my local XP work station! In fact the tests seem to indicate that it doesn't matter how many threads are generated (tests were ran with the app spawning between 2 to 32 threads). The algorithm always runs significantly slower on the UAT W2003 server? How could this be? Surely the app should run faster on a 8 cpu (Quad-Core) than my 2 Quad work station? Why are we seeing no performance gains with the multi-threading on the W2003 server whilst the XP workstation tests show gains of up to 40%? Any help or pointers would be appreciated. Regards Myles

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  • Removing a platform from Configuration Manager

    - by demoncodemonkey
    I have a solution containing C# and C++/CLI projects. There are 3 platforms in my solution: Any CPU Win32 Mixed Platforms I never want to "just build the C# ones" or "just build the C++ ones", I always want to build all projects. So the platforms metaphor is meaningless to me, I'll leave it on Mixed Platforms or whatever as long as they all build. Now VS sometimes automatically switches the current platform to Any CPU (I'm not sure when or why). This means that pressing F7 will only try to build the C# projects, which is obviously no good. So I have to switch back to Mixed Platforms and try again. So how to workaround this irritating problem? I have tried 2 ways: In Configuration Manager, remove Any CPU and Win32 platforms. This worked until I added a new project and Visual Studio very kindly added them back in... :/ In Configuration Manager, check all checkboxes for all projects in all configurations in all platforms. This becomes a nightmare to manage with many projects in the solution. Any other ideas?

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  • Opengl Iphone SDK: How to tell if you're touching an object on screen?

    - by TheGambler
    First is my touchesBegan function and then the struct that stores the values for my object. I have an array of these objects and I'm trying to figure out when I touch the screen if I'm touching an object on the screen. I don't know if I need to do this by iterating through all my objects and figure out if I'm touching an object that way or maybe there is an easier more efficient way. How is this usually handled? -(void)touchesBegan:(NSSet *)touches withEvent:(UIEvent *)event{ [super touchesEnded:touches withEvent:event]; UITouch* touch = ([touches count] == 1 ? [touches anyObject] : nil); CGRect bounds = [self bounds]; CGPoint location = [touch locationInView:self]; location.y = bounds.size.height - location.y; float xTouched = location.x/20 - 8 + ((int)location.x % 20)/20; float yTouched = location.y/20 - 12 + ((int)location.y % 20)/20; } typedef struct object_tag // Create A Structure Called Object { int tex; // Integer Used To Select Our Texture float x; // X Position float y; // Y Position float z; // Z Position float yi; // Y Increase Speed (Fall Speed) float spinz; // Z Axis Spin float spinzi; // Z Axis Spin Speed float flap; // Flapping Triangles :) float fi; // Flap Direction (Increase Value) } object;

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  • How close can I get C# to the performance of C++ for small intensive tasks?

    - by SLC
    I was thinking about the speed difference of C++ to C# being mostly about C# compiling to byte-code that is taken in by the JIT compiler (is that correct?) and all the checks C# does. I notice that it is possible to turn a lot of these functions off, both in the compile options, and possibly through using the unsafe keyword as unsafe code is not verifiable by the common language runtime. Therefore if you were to write a simple console application in both languages, that flipped an imaginary coin an infinite number of times and displayed the results to the screen every 10,000 or so iterations, how much speed difference would there be? I chose this because it's a very simple program. I'd like to test this but I don't know C++ or have the tools to compile it. This is my C# version though: static void Main(string[] args) { unsafe { Random rnd = new Random(); int heads = 0, tails = 0; while (true) { if (rnd.NextDouble() > 0.5) heads++; else tails++; if ((heads + tails) % 1000000 == 0) Console.WriteLine("Heads: {0} Tails: {1}", heads, tails); } } } Is the difference enough to warrant deliberately compiling sections of code "unsafe" or into DLLs that do not have some of the compile options like overflow checking enabled? Or does it go the other way, where it would be beneficial to compile sections in C++? I'm sure interop speed comes into play too then. To avoid subjectivity, I reiterate the specific parts of this question as: Does C# have a performance boost from using unsafe code? Do the compile options such as disabling overflow checking boost performance, and do they affect unsafe code? Would the program above be faster in C++ or negligably different? Is it worth compiling long intensive number-crunching tasks in a language such as C++ or using /unsafe for a bonus? Less subjectively, could I complete an intensive operation faster by doing this?

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  • jQuery Collapse (with cookies), default open instead of closed?

    - by Christian
    Hi. I've got a jQuery snippet which basically allows a user to toggle a div, open or closed - their preference is saved in a cookie. (function($) { $.fn.extend({ collapse: function(options) { var defaults = { inactive : "inactive", active : "active", head : ".trigger", group : ".wrap-me-up", speed : 300, cookie : "collapse" }; // Set a cookie counter so we dont get name collisions var op = $.extend(defaults, options); cookie_counter = 0; return this.each(function() { // Increment cookie name counter cookie_counter++; var obj = $(this), sections = obj.find(op.head).addClass(op.inactive), panel = obj.find(op.group).hide(), l = sections.length, cookie = op.cookie + "_" + cookie_counter; // Look for existing cookies for (c=0;c<=l;c++) { var cvalue = $.cookie(cookie + c); if ( cvalue == 'open' + c ) { panel.eq(c).show(); panel.eq(c).prev().removeClass(op.inactive).addClass(op.active); }; }; sections.click(function(e) { e.preventDefault(); var num = sections.index(this); var cookieName = cookie + num; var ul = $(this).next(op.group); // If item is open, slide up if($(this).hasClass(op.active)) { ul.slideUp(op.speed); $(this).removeClass(op.active).addClass(op.inactive); $.cookie(cookieName, null, { path: '/', expires: 10 }); return } // Else slide down ul.slideDown(op.speed); $(this).addClass(op.active).removeClass(op.inactive); var cookieValue = 'open' + num; $.cookie(cookieName, cookieValue, { path: '/', expires: 10 }); }); }); } }); })(jQuery); Demo: http://christianbullock.com/demo/ I'm just wondering how I can display the list open as default, and have the div collapse when the header is clicked? Many thanks. Christian.

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