multicore

Unable to delete previous doSMP queues

由 安稳与你 提交于 2019-12-02 00:57:33
I'm trying to use doSMP, and when I try w <- startWorkers(4) , I get the error 1: In startWorkers(workerCount = 4) : there is an existing doSMP session using doSMP1 (actually doSMP1,...doSMP8). Now, when I try to remove this using rmSessions('doSMP1') I get the error message attempting to delete qnames: doSMP1 unable to delete queues: doSMP1 Any suggestions on how to get this to work. On my 8-core machine, doSNOW stopped working from version 2.11, and I would like to be able to parallel process locally without sending things out to a linux server. I'm running R 2.12.1 (32-bit) on WinXP 64-bit

Multi core CPU single thread behaviour, not achieving 100%

由 送分小仙女□ 提交于 2019-12-01 21:18:42
问题 As you can see from the attached image the CPU graph on my dual core machine is weirdly symmetrical! Is this some sort of load balancing to prevent one core being used more than the other? What are the reasons behind it (heat distribution maybe)? Of course my main concern: is my single thread PSNR image algorithm achieving 100%? CPU is Core 2 Duo E6850 3Ghz running Ubuntu 10.4. Thanks Ross 回答1: You are achieving a 50% load using both CPUs. Your program is not attached to a fixed CPU so it's

How can I get R to use more CPU usage?

由 僤鯓⒐⒋嵵緔 提交于 2019-12-01 20:52:22
问题 I noticed that R doesn't use all of my CPU, and I want to increase that tremendously (upwards to 100%). I don't want it to just parallelize a few functions; I want R to use more of my CPU resources. I am trying to run a pure IP set packing program using the lp() function. Currently, I run windows and I have 4 cores on my computer. I have tried to experiment with snow, doParallel, and foreach (though I do not know what I am doing with them really). In my code I have this... library(foreach)

How can I get R to use more CPU usage?

由 ╄→гoц情女王★ 提交于 2019-12-01 18:36:01
I noticed that R doesn't use all of my CPU, and I want to increase that tremendously (upwards to 100%). I don't want it to just parallelize a few functions; I want R to use more of my CPU resources. I am trying to run a pure IP set packing program using the lp() function. Currently, I run windows and I have 4 cores on my computer. I have tried to experiment with snow, doParallel, and foreach (though I do not know what I am doing with them really). In my code I have this... library(foreach) library(doParallel) library(snowfall) cl <- makeCluster(4) registerDoParallel(cl) sfInit(parallel = TRUE,

golang多核陷阱一例

由 三世轮回 提交于 2019-12-01 09:51:11
同时发布在 独立博客 。 以前一直以为，在Golang中，针对高并发的情况，采用多核处理一定效果最优，但是项目实践证明事实不是这样的。 在 Sniper 项目中(一个结合了 ab 和 siege 优点的http负载测试工具)，原来一直设置cup使用数为系统cpu总数： runtime.GOMAXPROCS(runtime.NumCPU()) 在与ab的性能比较中一直有较大差距，GET请求局域网的一个10k大小的文件： 以下是ab的性能，并发100，总请求100k，执行时间16.082秒 Concurrency Level: 100 Time taken for tests: 16.082 seconds Complete requests: 100000 Failed requests: 0 Write errors: 0 Total transferred: 1035500000 bytes HTML transferred: 1024000000 bytes Requests per second: 6218.04 [#/sec] (mean) Time per request: 16.082 [ms] (mean) Time per request: 0.161 [ms] (mean, across all concurrent requests) Transfer rate

force some data on L1 cache

由 南楼画角 提交于 2019-12-01 08:23:23
Apologies about this simple question. Still struggling with some of the memory concepts here. Question is: Suppose I have a pre-computed array A that I want to access repeatedly. Is there a way to tell a C program to keep this array as close as possible to the CPU cache for fastest access? Thanks. There is no way to force an array to L1/L2 cache on most architectures; it is not needed usually, if you access it frequently it is unlikely to be evicted from cache. On some architectures there is a set of instructions that allows you to give the processor a hint that the memory location will soon

armadillo linear system solver (with openblas)

由 强颜欢笑 提交于 2019-12-01 06:14:55
问题 I've been testing various open source codes for solving a linear system of equations in C++. So far the fastest I've found is armadillo, using the OPENblas package as well. To solve a dense linear NxN system, where N=5000 takes around 8.3 seconds on my system, which is really really fast (without openblas installed, it takes around 30 seconds). One reason for this increase is that armadillo+openblas seems to enable using multiple threads. It runs on two of my cores, whereas armadillo without

Semantics of Thread.currentThread() on multicore/multi processor systems?

由 血红的双手。 提交于 2019-12-01 06:09:41
问题 If running on a multicore or multi processor machine where the jvm has the potential to run more than one thread absolutely simultaneously (not just apparent simultaneously), what does the api method java.lang.Thread.currentThread() return?....in the above scenario, does it just return one of the current threads at random? 回答1: It returns the thread you are currently running inside. If you have two cores and two threads A and B running completely concurrently, calling this method at the same

How to reserve a core for one thread on windows?

由 …衆ロ難τιáo~ 提交于 2019-12-01 05:50:48
问题 I am working on a very time sensitive application which polls a region of shared memory taking action when it detects a change has occurred. Changes are rare but I need to minimize the time from change to action. Given the infrequency of changes I think the CPU cache is getting cold. Is there a way to reserve a core for my polling thread so that it does not have to compete with other threads for either cache or CPU? 回答1: Thread affinity alone ( SetThreadAffinityMask ) will not be enough. It

force some data on L1 cache

由 限于喜欢 提交于 2019-12-01 05:47:43
问题 Apologies about this simple question. Still struggling with some of the memory concepts here. Question is: Suppose I have a pre-computed array A that I want to access repeatedly. Is there a way to tell a C program to keep this array as close as possible to the CPU cache for fastest access? Thanks. 回答1: There is no way to force an array to L1/L2 cache on most architectures; it is not needed usually, if you access it frequently it is unlikely to be evicted from cache. On some architectures
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