multicore

I know in perfmon you can see how much each core is utilized, and how much total CPU a particular process is using. However I can't seem to find a way to see how much CPU a process is using broken down by cores. Is there a built-in way to see this information? Is there a programmatic way to see this? (C# preferred) Am I demonstrating a misunderstanding of how Windows (Vista) handles core usage and context switching by asking this question? Edit: More clarification of what I want to find out. Is my process only using 30-40% total CPU (on a quad core) because it's not sufficiently multi-threaded

I was wondering if there is multi core support for the OpenCV library. I am experimenting with Haar cascading and it's pretty slow on my Raspberry 2 which would have four cores, but my application is currently running only on a single one. Any ideas? CascadedDetect has multi core support. Recompile OpenCV with the WITH_TBB or WITH_OPENMP (or any other threading framework supported by OpenCV) flag on to enable it. I've had a brief look at the class CascadeClassifier in modules\objdetect\src\cascadedetect.cpp , and there seems to be a reasonable amount of implementation done with parallel_for_ ,

This question already has answers here : Closed 7 years ago . Possible Duplicate: Parallel processing in R limited I've written some code in R multicore, and I'm running it on a 24-core machine. In fact there are only 12 cores, but they are hyperthreaded, so it looks like there are 24. Here's what's strange: all the threads run on the same single core! So they each only use a tiny amount of cpu, instead of each running on a single core, and chewing up all available cores. For simplicity, I'm just running 4 threads: mclapply( 1:30, function(size) { # time consuming stuff that is cpu bound

I have been reading and trying to understand how does Spark framework use its cores in Standalone mode. According to Spark documentation, the parameter " spark.task.cpus "'s value is set to be 1 by default, which means number of cores to allocate for each task. Question 1: For a multi-core machine (e.g., 4 cores in total, 8 hardware threads), when "spark.task.cpus = 4", will Spark use 4 cores (1 thread per core) or 2 cores with hyper-thread? What will it happen if I set "spark.task.cpus = 16", more than the number of available hardware threads on this machine? Question 2: How is this type of

Perl's documentation says: Since Perl 5.8, thread programming has been available using a model called interpreter threads which provides a new Perl interpreter for each thread Using ps -Lm <pid> with the program below I can see that threads run in parallel, i.e., they are being run at the same time in different cores. But even when there are 4 threads (3 and the main) ps aux shows only one Perl process. Does this mean that there is a whole Perl interpreter on each thread? Are Perl threads mapped to system threads ? If 2 is true, how is possible to have multiple Perl interpreters within a

What x86-64 instructions are used to enable/disable other cores/processors and how does one start executing code on them? Is there documentation somewhere on how this is done by the operating system? Pretty painful to get an x86 up and going... it is not so much in the cores as in the APIC system. You need to look into the docs for your chipset, tends to be pretty much hidden unfortunately. You will have to be at the kernel level, definitely. Looking at Linux sounds like a good idea. Assuming you're talking about implementing a kernel.... My understanding is it's largely based on this document

I've read about spark and I found out that spark is written in scala. Since scala is a functional language, like erlang, it can use multi core properly. Is that correct? I'm wondering if I can use spark in distributed system which have multicore processors. Can a single task use all cores at the same time? I've read YARN will assign different cores on each different tasks, but in this case, it is a single task And, is it just enough to use multi thread programming in JAVA (hadoop) to use all cores in each computer? Since linux scheduler is based on threads? Mischa Arefiev Yes, it can, as this

The makeCluster function for the SNOW package has the different cluster types of " SOCK ", " PVM ", " MPI ", and " NWS " but I'm not very clear on the differences among them, and more specifically which would be best for my program. Currently I have a queue of tasks of different length going into a load balancing cluster with clusterApplyLB and am using a 64bit 32-core Windows machine. I am looking for a brief description of the differences among the four cluster types, which would be best for my use and why. Welcome to parallel programming. You may want to peruse the vignette of the excellent