So are you suggesting that real numerical workloads under BLAS.set_num_threads(4) will indeed be faster than under BLAS.set_num_threads(2)? That hasn't been my experience and I figured the peakflops() example would constitute an MWE. Is there another workload you would suggest I try to figure out if this is just a peak flops() aberration or a real issue?
On Wednesday, October 19, 2016 at 8:28:16 PM UTC-5, Ralph Smith wrote: > > At least 2 things contribute to erratic results from peakflops(). With > hyperthreading, the threads are not always put on separate cores. Secondly, > the measured time includes > the allocation of the result matrix, so garbage collection affects some of > the results. Most available advice says to disable hyperthreading on > dedicated number crunchers > (most full loads work slightly more efficiently without the extra context > switching). The GC issue seems to be a mistake, if "peak" is to be taken > seriously. > > On Wednesday, October 19, 2016 at 12:04:00 PM UTC-4, Thomas Covert wrote: >> >> I have a recent iMac with 4 logical cores (and 8 hyper threads). I would >> have thought that peakflops(N) for a large enough N should be increasing in >> the number of threads I allow BLAS to use. I do find that peakflops(N) >> with 1 thread is about half as high as peakflops(N) with 2 threads, but >> there is no gain to 4 threads. Are my expectations wrong here, or is it >> possible that BLAS is somehow configured incorrectly on my machine? In the >> example below, N = 6755, a number relevant for my work, but the results are >> similar with 5000 or 10000. >> >> here is my versioninfo() >> julia> versioninfo() >> Julia Version 0.5.0 >> Commit 3c9d753* (2016-09-19 18:14 UTC) >> Platform Info: >> System: Darwin (x86_64-apple-darwin15.6.0) >> CPU: Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz >> WORD_SIZE: 64 >> BLAS: libopenblas (DYNAMIC_ARCH NO_AFFINITY Haswell) >> LAPACK: libopenblas >> LIBM: libopenlibm >> LLVM: libLLVM-3.7.1 (ORCJIT, haswell) >> >> here is an example peakflops() exercise: >> julia> BLAS.set_num_threads(1) >> >> julia> mean(peakflops(6755) for i=1:10) >> 5.225580459387056e10 >> >> julia> BLAS.set_num_threads(2) >> >> julia> mean(peakflops(6755) for i=1:10) >> 1.004317640281997e11 >> >> julia> BLAS.set_num_threads(4) >> >> julia> mean(peakflops(6755) for i=1:10) >> 9.838116463900085e10 >> >> >> >> >> >>
