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  • 75 to 100% of CPU Usage in WPF?

    - by Khan
    Hi, Whenever application loads and any other usercontrol loads in the application, while loading and rendering the cpu usage touches 80 - 100%. How should i resolve this? Thanks and regards, Ershad

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  • Does acos, atan functions in stl uses lots of cpu cycles

    - by jan
    Hi all, I wanted to calculate the angle between two vectors but I have seen these inverse trig operations such as acos and atan uses lots of cpu cycles. Is there a way where I can get this calculation done without using these functions? Also, does these really hit you when you in your optimization?

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  • Random RAM usage amounts

    - by DTown
    I was hoping someone could explain why my application when loaded uses varying amounts of RAM. I'm speaking about a compiled version that uses the exe directly. It's a pretty basic applications and there are no conditional branches in the startup of the application. Yet every time I start it up the RAM amount varies from 6MB-16MB. I know it's on the small end of usage anyways but I'm curious of why this happens.

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  • High accuracy cpu timers

    - by John Robertson
    An expert in highly optimized code once told me that an important part of his strategy was the availability of extremely high performance timers on the CPU. Does anyone know what those are and how one can access them to test various code optimizations? While I am interested regardless, I also wanted to ask whether it is possible to access them from something higher than assembly (or with only a little assembly) via visual studio C++?

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  • Manually Increasing the Amount of CPU a Java Application Uses

    - by SkylineAddict
    I've just made a program with Eclipse that takes a really long time to execute. It's taking even longer because it's loading my CPU to 25% only (I'm assuming that is because I'm using a quad-core and the program is only using one core). Is there any way to make the program use all 4 cores to max it out? Java is supposed to be natively multi-threaded, so I don't understand why it would only use 25%.

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  • mysql: managing memory usage

    - by every_answer_gets_a_point
    i am doing a delete with a LIKE statement my keybuffer is 25m, the sort buffer size is 256k the delete has been taking over 2 hours should i increase memory usage? there are about 50 megs of data in the table from which i am deleting, thats about 500,000 rows is there anything else i can do on the adminsitration size to speed up this delete?

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  • Windows Game Loop 50% CPU on Dual Core

    - by Dave18
    The game loop alone is using 50% of CPU Usage, I haven't done any rendering work yet. What i'm doing here? while(true) { if(PeekMessage(&msg,NULL,0,0,PM_REMOVE)) { if(msg.message == WM_QUIT || msg.message == WM_CLOSE || msg.message == WM_DESTROY) break; TranslateMessage(&msg); DispatchMessage(&msg); } else { //Run game code, break out of loop when the game is over } }

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  • Named pipe is using 100% CPU

    - by willwill
    I'm starting the script with ./file.py < pipe >> logfile and the script is: while True: try: I = raw_input().strip().split() except EOFError: continue doSomething() How could I better handle named pipe? This script always run at 100% CPU and it need to be real-time so I cannot use time.sleep.

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  • Python : How do you find the CPU consumption for a piece of code?

    - by Yugal Jindle
    Background: I have a django application, it works and responds pretty well on low load, but on high load like 100 users/sec, it consumes 100% CPU and then due to lack of CPU slows down. Problem : Profiling the application gives me time taken by functions. This time increases on high load. Time consumed may be due to complex calculation or for waiting for CPU. so, how to find the CPU cycles consumed by a piece of code ? Since, reducing the CPU consumption will increase the response time. I might have written extremely efficient code and need to add more CPU power OR I might have some stupid code taking the CPU and causing the slow down ? Any help is appreciated ! Update: I am using Jmeter to profile my webapp, it gives me a throughput of 2 requests/sec. [ 100 users] I get a average time of 36 seconds on 100 request vs 1.25 sec time on 1 request. More Info Configuration Nginx + Uwsgi with 4 workers No database used, using a responses from a REST API On 1st hit the response of REST API gets cached, therefore doesn't makes a difference. Using ujson for json parsing. Curious to Know: Python-Django is used by so many orgs for so many big sites, then there must be some high end Debug / Memory-CPU analysis tools. All those I found were casual snippets of code that perform profiling.

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • Can the STREAM and GUPS (single CPU) benchmark use non-local memory in NUMA machine

    - by osgx
    Hello I want to run some tests from HPCC, STREAM and GUPS. They will test memory bandwidth, latency, and throughput (in term of random accesses). Can I start Single CPU test STREAM or Single CPU GUPS on NUMA node with memory interleaving enabled? (Is it allowed by the rules of HPCC - High Performance Computing Challenge?) Usage of non-local memory can increase GUPS results, because it will increase 2- or 4- fold the number of memory banks, available for random accesses. (GUPS typically limited by nonideal memory-subsystem and by slow memory bank opening/closing. With more banks it can do update to one bank, while the other banks are opening/closing.) Thanks. UPDATE: (you may nor reorder the memory accesses that the program makes). But can compiler reorder loops nesting? E.g. hpcc/RandomAccess.c /* Perform updates to main table. The scalar equivalent is: * * u64Int ran; * ran = 1; * for (i=0; i<NUPDATE; i++) { * ran = (ran << 1) ^ (((s64Int) ran < 0) ? POLY : 0); * table[ran & (TableSize-1)] ^= stable[ran >> (64-LSTSIZE)]; * } */ for (j=0; j<128; j++) ran[j] = starts ((NUPDATE/128) * j); for (i=0; i<NUPDATE/128; i++) { /* #pragma ivdep */ for (j=0; j<128; j++) { ran[j] = (ran[j] << 1) ^ ((s64Int) ran[j] < 0 ? POLY : 0); Table[ran[j] & (TableSize-1)] ^= stable[ran[j] >> (64-LSTSIZE)]; } } The main loop here is for (i=0; i<NUPDATE/128; i++) { and the nested loop is for (j=0; j<128; j++) {. Using 'loop interchange' optimization, compiler can convert this code to for (j=0; j<128; j++) { for (i=0; i<NUPDATE/128; i++) { ran[j] = (ran[j] << 1) ^ ((s64Int) ran[j] < 0 ? POLY : 0); Table[ran[j] & (TableSize-1)] ^= stable[ran[j] >> (64-LSTSIZE)]; } } It can be done because this loop nest is perfect loop nest. Is such optimization prohibited by rules of HPCC?

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  • C# Threading vs single thread

    - by user177883
    Is it always guaranteed that a multi-threaded application would run faster than a single threaded application? I have two threads that populates data from a data source but different entities (eg: database, from two different tables), seems like single threaded version of the application is running faster than the version with two threads. Why would the reason be? when i look at the performance monitor, both cpu s are very spikey ? is this due to context switching? what are the best practices to jack the CPU and fully utilize it? I hope this is not ambiguous.

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  • Threading vs single thread

    - by user177883
    Is it always guaranteed that a multi-threaded application would run faster than a single threaded application? I have two threads that populates data from a data source but different entities (eg: database, from two different tables), seems like single threaded version of the application is running faster than the version with two threads. Why would the reason be? when i look at the performance monitor, both cpu s are very spikey ? is this due to context switching? what are the best practices to jack the CPU and fully utilize it? I hope this is not ambiguous.

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  • tomcat multithreading problem

    - by jutky
    Hi all I'm writing a java application that runs in Tomcat, on a multi-core hardware. The application executes an algorithm and returns the answer to the user. The problem is that even when I run two requests simultaneously, the tomcat process uses at most one CPU core. As far as I understand each request in Tomcat is executed in separate thread, and JVM should run each thread on separate CPU core. What could be the problem that bounds the JVM or Tomcat to use no more than one core? Thanks in advance.

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  • How to populate RRD database with CPU and MEM usage data?

    - by Tomaszs
    I have a Lighttpd server (on Centos) and would like to display 4 graphs: lighttpd traffic, lighttpd requests per second, CPU usage and MEM usage. I've set place for rrd database for lighttpd config like this: rrdtool.binary = "/usr/bin/rrdtool" rrdtool.db-name = "/var/www/lighttpd.rrd" And put into my WWW cgi-bin sh file that gets data from lighttpd RRD file and creates graphs of traffic and requests per second like this: #!/bin/sh RRDTOOL=/usr/bin/rrdtool OUTDIR=//var/www/graphs INFILE=/var/www/lighttpd.rrd OUTPRE=lighttpd-traffic WIDTH=400 HEIGHT=100 DISP="-v bytes --title TrafficWebserver \ DEF:binraw=$INFILE:InOctets:AVERAGE \ DEF:binmaxraw=$INFILE:InOctets:MAX \ DEF:binminraw=$INFILE:InOctets:MIN \ DEF:bout=$INFILE:OutOctets:AVERAGE \ DEF:boutmax=$INFILE:OutOctets:MAX \ DEF:boutmin=$INFILE:OutOctets:MIN \ CDEF:bin=binraw,-1,* \ CDEF:binmax=binmaxraw,-1,* \ CDEF:binmin=binminraw,-1,* \ CDEF:binminmax=binmaxraw,binminraw,- \ CDEF:boutminmax=boutmax,boutmin,- \ AREA:binmin#ffffff: \ STACK:binmax#f00000: \ LINE1:binmin#a0a0a0: \ LINE1:binmax#a0a0a0: \ LINE2:bin#efb71d:incoming \ GPRINT:bin:MIN:%.2lf \ GPRINT:bin:AVERAGE:%.2lf \ GPRINT:bin:MAX:%.2lf \ AREA:boutmin#ffffff: \ STACK:boutminmax#00f000: \ LINE1:boutmin#a0a0a0: \ LINE1:boutmax#a0a0a0: \ LINE2:bout#a0a735:outgoing \ GPRINT:bout:MIN:%.2lf \ GPRINT:bout:AVERAGE:%.2lf \ GPRINT:bout:MAX:%.2lf \ " $RRDTOOL graph $OUTDIR/$OUTPRE-hour.png -a PNG --start -14400 $DISP -w $WIDTH -h $HEIGHT $RRDTOOL graph $OUTDIR/$OUTPRE-day.png -a PNG --start -86400 $DISP -w $WIDTH -h $HEIGHT $RRDTOOL graph $OUTDIR/$OUTPRE-month.png -a PNG --start -2592000 $DISP -w $WIDTH -h $HEIGHT OUTPRE=lighttpd-requests DISP="-v req --title RequestsperSecond -u 1 \ DEF:req=$INFILE:Requests:AVERAGE \ DEF:reqmax=$INFILE:Requests:MAX \ DEF:reqmin=$INFILE:Requests:MIN \ CDEF:reqminmax=reqmax,reqmin,- \ AREA:reqmin#ffffff: \ STACK:reqminmax#00f000: \ LINE1:reqmin#a0a0a0: \ LINE1:reqmax#a0a0a0: \ LINE2:req#00a735:requests" $RRDTOOL graph $OUTDIR/$OUTPRE-hour.png -a PNG --start -14400 $DISP -w $WIDTH -h $HEIGHT $RRDTOOL graph $OUTDIR/$OUTPRE-day.png -a PNG --start -86400 $DISP -w $WIDTH -h $HEIGHT $RRDTOOL graph $OUTDIR/$OUTPRE-month.png -a PNG --start -2592000 $DISP -w $WIDTH -h $HEIGHT Basically it's not my script, i get it from somewhere from the internet. Now i would like to do the same for CPU usage and MEM usage. I don't like to use any additional packages! As you can see lighttpd populates lighttpd.rrd file with traffic data and requests per second. Now i would like to the system to populate second rrd file with CPU and MEM usage, so i can add to sh file code to generate graphs for this data. How can I populate RRD file with CPU and MEM usage data? Please, NO THIRD-PARTY tools !

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  • How to solve High Load average issue in Linux systems?

    - by RoCkStUnNeRs
    The following is the different load with cpu time in different time limit . The below output has parsed from the top command. TIME LOAD US SY NICE ID WA HI SI ST 12:02:27 208.28 4.2%us 1.0%sy 0.2%ni 93.9%id 0.7%wa 0.0%hi 0.0%si 0.0%st 12:23:22 195.48 4.2%us 1.0%sy 0.2%ni 93.9%id 0.7%wa 0.0%hi 0.0%si 0.0%st 12:34:55 199.15 4.2%us 1.0%sy 0.2%ni 93.9%id 0.7%wa 0.0%hi 0.0%si 0.0%st 13:41:50 203.66 4.2%us 1.0%sy 0.2%ni 93.8%id 0.8%wa 0.0%hi 0.0%si 0.0%st 13:42:58 278.63 4.2%us 1.0%sy 0.2%ni 93.8%id 0.8%wa 0.0%hi 0.0%si 0.0%st Following is the additional Information of the system? cat /proc/cpuinfo processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 23 model name : Intel(R) Xeon(R) CPU E5410 @ 2.33GHz stepping : 10 cpu MHz : 1992.000 cache size : 6144 KB physical id : 0 siblings : 4 core id : 0 cpu cores : 4 apicid : 0 initial apicid : 0 fdiv_bug : no hlt_bug : no f00f_bug : no coma_bug : no 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 lm constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr dca sse4_1 lahf_lm bogomips : 4658.69 clflush size : 64 power management: processor : 1 vendor_id : GenuineIntel cpu family : 6 model : 23 model name : Intel(R) Xeon(R) CPU E5410 @ 2.33GHz stepping : 10 cpu MHz : 1992.000 cache size : 6144 KB physical id : 0 siblings : 4 core id : 1 cpu cores : 4 apicid : 1 initial apicid : 1 fdiv_bug : no hlt_bug : no f00f_bug : no coma_bug : no 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 lm constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr dca sse4_1 lahf_lm bogomips : 4655.00 clflush size : 64 power management: processor : 2 vendor_id : GenuineIntel cpu family : 6 model : 23 model name : Intel(R) Xeon(R) CPU E5410 @ 2.33GHz stepping : 10 cpu MHz : 1992.000 cache size : 6144 KB physical id : 0 siblings : 4 core id : 2 cpu cores : 4 apicid : 2 initial apicid : 2 fdiv_bug : no hlt_bug : no f00f_bug : no coma_bug : no 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 lm constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr dca sse4_1 lahf_lm bogomips : 4655.00 clflush size : 64 power management: processor : 3 vendor_id : GenuineIntel cpu family : 6 model : 23 model name : Intel(R) Xeon(R) CPU E5410 @ 2.33GHz stepping : 10 cpu MHz : 1992.000 cache size : 6144 KB physical id : 0 siblings : 4 core id : 3 cpu cores : 4 apicid : 3 initial apicid : 3 fdiv_bug : no hlt_bug : no f00f_bug : no coma_bug : no 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 lm constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr dca sse4_1 lahf_lm bogomips : 4654.99 clflush size : 64 power management: Memory: total used free shared buffers cached Mem: 2 1 1 0 0 0 Swap: 5 0 5 let me know why the system is getting abnormally this much high load?

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  • Java native methods issues with SUN JVM (jdk1.5.0_14) and multi-core CPU’s

    - by Mattias Arnersten
    We are hosting an application on SUN JVM that handles a lot of XML parsing using Jaxb. The application is parsing the XML fine using JRockit 5 but when using the SUN JVM the JVM spends a majority of it’s time on native methods such as java-lang.System.arraycopy, java.lang.String.intern and java.lang.ClassLoader.getPackage. The CPU load is approx. 60% higher when using SUN JVM compared with JRockit. Even stranger is that when we only run the application server using one core (in WMWare) the problem disappears. Has anyone experienced the same behavior? Mattias Arnersten

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