问题
I am developing large dense matrix multiplication code. When I profile the code it sometimes gets about 75% of the peak flops of my four core system and other times gets about 36%. The efficiency does not change between executions of the code. It either starts at 75% and continues with that efficiency or starts at 36% and continues with that efficiency.
I have traced the problem down to hyper-threading and the fact that I set the number of threads to four instead of the default eight. When I disable hyper-threading in the BIOS I get about 75% efficiency consistently (or at least I never see the drastic drop to 36%).
Before I call any parallel code I do omp_set_num_threads(4). I have also tried export OMP_NUM_THREADS=4 before I run my code but it seems to be equivalent.
I don't want to disable hyper-threading in the BIOS. I think I need to bind the four threads to the four cores. I have tested some different cases of GOMP_CPU_AFFINITY but so far I still have the problem that the efficiency is 36% sometimes. What is the mapping with hyper-threading and cores? E.g. do thread 0 and thread 1 correspond to the the same core and thread 2 and thread 3 another core?
How can I bind the threads to each core without thread migration so that I don't have to disable hyper-threading in the BIOS? Maybe I need to look into using sched_setaffinity?
Some details of my current system: Linux kernel 3.13, GCC 4.8,Intel Xeon E5-1620 (four physical cores, eight hyper-threads).
Edit: This seems to be working well so far
export GOMP_CPU_AFFINITY="0 1 2 3 4 5 6 7"
or
export GOMP_CPU_AFFINITY="0-7"
Edit: This seems also to work well
export OMP_PROC_BIND=true
Edit: These options also work well (gemm is the name of my executable)
numactl -C 0,1,2,3 ./gemm
and
taskset -c 0,1,2,3 ./gemm
回答1:
This isn't a direct answer to your question, but it might be worth looking in to: apparently, hyperthreading can cause your cache to thrash. Have you tried checking out valgrind to see what kind of issue is causing your problem? There might be a quick fix to be had from allocating some junk at the top of every thread's stack so that your threads don't end up kicking each others cache lines out.
It looks like your CPU is 4-way set associative so it's not insane to think that, across 8 threads, you might end up with some really unfortunately aligned accesses. If your matrices are aligned on a multiple of the size of your cache, and if you had pairs of threads accessing areas a cache-multiple apart, any incidental read by a third thread would be enough to start causing conflict misses.
For a quick test -- if you change your input matrices to something that's not a multiple of your cache size (so they're no longer aligned on a boundary) and your problems disappear, then there's a good chance that you're dealing with conflict misses.
来源：https://stackoverflow.com/questions/24368576/poor-performance-due-to-hyper-threading-with-openmp-how-to-bind-threads-to-core