Thanks for digging that out Kellen. That's good info so maybe it would be good to rework the fix with the info you provided and remove the pthread_atfork handlers. Do you think setting the device would avoid the problem seen on the backtrace you provided? specifically here: https://gist.github.com/KellenSunderland/893d11165e19d1efcf5c0fe8e8584600#file-hang_bt-L24
On Mon, Jun 24, 2019 at 6:43 PM kellen sunderland <[email protected]> wrote: > > I remember at the time we also had a read through of this blog post, but to > use the code looked like it was following the advice: > https://devblogs.nvidia.com/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/ > > On Mon, Jun 24, 2019 at 6:39 PM kellen sunderland < > [email protected]> wrote: > > > I remember this hang as well, it was pretty hard to reproduce IIRC. I > > believe the stacks for the hang are here: > > https://gist.github.com/KellenSunderland/893d11165e19d1efcf5c0fe8e8584600 > > and > > the trick was we could only debug it up to the point that we hit: > > > > #0 0x00007fec6df1ba4f in futex_wait (private=0, expected=1, > > futex_word=0x7fec60843758) > > at ../sysdeps/unix/sysv/linux/futex-internal.h:61 > > #1 futex_wait_simple (private=0, expected=1, futex_word=0x7fec60843758) > > at ../sysdeps/nptl/futex-internal.h:135 > > #2 __pthread_once_slow (once_control=0x7fec60843758, > > init_routine=0x7fec605f38f0) > > at pthread_once.c:105 > > ... > > #6 0x00007fec6061c577 in cudaSetDevice () from > > /usr/local/cuda/lib64/libcudart.so.9.0 > > > > because the code in libcudart is obviously closed source we couldn't dig > > into what threading work was going on when we called cudaSetDevice. > > > > On Mon, Jun 24, 2019 at 6:13 PM Pedro Larroy <[email protected]> > > wrote: > > > >> If you check initialize.cc we seem to be explicitly disabling that > >> behaviour in pthread_at_fork which seems to cause thread contention > >> during multiprocessing. Why do we need this major advantage for the > >> library if that's the case? > >> > >> Related PRs: > >> > >> https://github.com/apache/incubator-mxnet/pull/10820 > >> https://github.com/apache/incubator-mxnet/issues/14396 > >> > >> The original code was authored in this PR: > >> > >> https://github.com/apache/incubator-mxnet/pull/8677 > >> > >> I actually remember this fix, it was done during a release as the cuda > >> runtime was forking and the engine was being re-entered. If that > >> situation is now happening anymore it might not be needed any longer. > >> I don't think we know the cause why there was a fork inside cuda, so > >> the code has grown around a fix for an issue which its root cause was > >> not understood, and side effects which this fix caused afterwards. > >> > >> My build uses MKL+LLVM OMP+DEBUG as seen in the container provided in > >> the link above, no libgomp. > >> > >> I didn't try the Make build. > >> > >> I would refactor the code linked above and stop using pthread_at_fork, > >> since OMP assumes it won't be initialized twice, but needs to be very > >> well tested to make sure it doesn't cause bugs or affect the fixes > >> done on the linked PRs above. > >> > >> Pedro. > >> > >> On Mon, Jun 24, 2019 at 5:38 PM Chris Olivier <[email protected]> > >> wrote: > >> > > >> > one major advantage of intel/llvm omp is that it spawns a new thread > >> pool > >> > after fork if a thread pool was already created. this is so that omp > >> can be > >> > used in the forked processes. libgomp doesn’t do this so it’ll just > >> lock up > >> > if you try to do omp in the forked process. > >> > > >> > is your build linking libgomp as well? > >> > > >> > standard mkl build (from Makefile) uses same omp library. are there > >> > problems with that build? > >> > > >> > what changes need to be made to make the assertion not fire? > >> > > >> > On Mon, Jun 24, 2019 at 5:32 PM Pedro Larroy < > >> [email protected]> > >> > wrote: > >> > > >> > > There's an assertion which is easily reproducible, and also there's a > >> > > crash including core dump, the latter is not easy to reproduce for me > >> > > in different environments. I have also seen mxnet getting stuck > >> > > without progressing with this build configuration and using no CPU at > >> > > all when running unit tests. > >> > > > >> > > In my view, the root cause of the assertion is that we are re-entering > >> > > OMP initialization when spawning threads on the following code through > >> > > pthread_at_fork > >> > > > >> > > > >> https://github.com/apache/incubator-mxnet/blob/master/src/initialize.cc#L58 > >> > > > >> > > This causes double initialization of the OMP engine, including the > >> > > assertion which you are asking about, and I suspect some additional > >> > > overhead. That's the shady forking part you are asking for. > >> > > > >> > > A question for you: What is the cause of runtime differences between > >> > > OMP runtimes? Shouldn't the implementation overhead diminish as > >> > > threads run longer? > >> > > > >> > > Pedro. > >> > > > >> > > On Mon, Jun 24, 2019 at 5:10 PM Chris Olivier <[email protected]> > >> > > wrote: > >> > > > > >> > > > What’s the reason for the assertion failure? btw classifying an > >> assertion > >> > > > failure a “crash” is debatable. As I stated in the original issue a > >> long > >> > > > time ago, it’s possible something shady is being done with when > >> forking > >> > > > that should be fixed. The assertion should be root caused. > >> > > > > >> > > > > >> > > > > >> > > > On Mon, Jun 24, 2019 at 1:22 PM Pedro Larroy < > >> > > [email protected]> > >> > > > wrote: > >> > > > > >> > > > > Added a dockerfile, and reports of a crash in my local machine > >> when > >> > > > > running MKL+OMP+DEBUG, with Anton's branch the crash happened as > >> well. > >> > > > > I couldn't reproduce the crash on my EC2 machine: > >> > > > > Added the backtrace of the crash as well. > >> > > > > > >> > > > > https://github.com/apache/incubator-mxnet/issues/10856 > >> > > > > > >> > > > > Dockerfile here: > >> > > > > > >> > > > > https://github.com/larroy/mxnet_omp > >> > > > > > >> > > > > Kind regards. > >> > > > > > >> > > > > Pedro. > >> > > > > > >> > > > > On Thu, Jun 20, 2019 at 5:29 PM Marco de Abreu < > >> > > [email protected]> > >> > > > > wrote: > >> > > > > > > >> > > > > > As already proposed, I think the easiest way to get a common > >> > > > > understanding > >> > > > > > is if we start with a few docker containers. Pedro, would it be > >> > > possible > >> > > > > > for you to wrap your benchmarks into a few containers that will > >> > > produce > >> > > > > > your shown results? That way, we can avoid possible > >> > > misunderstandings and > >> > > > > > also pinpoint the exact parts where people disagree or > >> misunderstood > >> > > each > >> > > > > > other. > >> > > > > > > >> > > > > > -Marco > >> > > > > > > >> > > > > > Pedro Larroy <[email protected]> schrieb am Do., > >> 20. Juni > >> > > > > 2019, > >> > > > > > 21:47: > >> > > > > > > >> > > > > > > I can confirm that we are linking with two versions of omp, > >> I'm > >> > > > > > > gaining more clarity into this topic, but I have still > >> questions, > >> > > the > >> > > > > > > facts that I got so far are the folllowing: > >> > > > > > > > >> > > > > > > * #1: We are linking with two versions of omp, intel's omp > >> and llvm > >> > > > > > > openmp when building with MKL enabled. > >> > > > > > > * #2: We have 3 different possible OMP versions: Intel OMP > >> (comes > >> > > with > >> > > > > > > MKL), LLVM OpenMP (3rdparty/openmp), libgomp (comes with gcc) > >> (This > >> > > > > > > one is used on the PR proposed by Anton). > >> > > > > > > > >> > > > > > > Questions: > >> > > > > > > > >> > > > > > > * #1 Is it ok to have two versions of openmp linked at the > >> same > >> > > time? > >> > > > > > > * #2 Which implementation of OMP gives the best > >> performance? (See > >> > > > > > > total training time of my measurement for a partial answer) > >> > > > > > > * #3 Should we have a build flag so we can choose the OMP > >> version > >> > > at > >> > > > > > > runtime? > >> > > > > > > * #4 Which Compiler and build flags did Chris use to get 10x > >> > > slowdown? > >> > > > > > > * #5 @Stas: is there a script to replicate your benchmarks > >> > > easily? If > >> > > > > > > so could you provide a link? I think we would need to > >> reproduce > >> > > your > >> > > > > > > benchmarks and verify which versions are being linked. It's > >> > > possible > >> > > > > > > that while compiling with MKL intel's omp was pulled in > >> instead of > >> > > > > > > GNU OpenMP. > >> > > > > > > * #6 @Chris: how to maintain the copy of LLVM's Openmp? > >> Should we > >> > > > > > > update the subrepo regularly? > >> > > > > > > > >> > > > > > > My conclusion so far: > >> > > > > > > > >> > > > > > > * #1 We should avoid linking two versions of omp if possible > >> and > >> > > > > > > allow users to choose one in the build as we do for BLAS. > >> > > > > > > * #2 For performance reasons and more control vs different > >> > > compiler > >> > > > > > > versions seems it makes indeed sense to keep the LLVM OpenMP > >> > > version > >> > > > > > > in 3rdparty for now. So unless some more data is gathered, it > >> makes > >> > > > > > > sense not to remove it as of now. > >> > > > > > > * #3 We should provide build options to choose which openmp > >> > > library > >> > > > > > > is to be used from the three options available, including > >> libgomp. > >> > > > > > > * #4 Refining the build we could also enable OpenMP in mac > >> without > >> > > > > > > additional contortions (doesn't work as of today): > >> > > > > > > https://iscinumpy.gitlab.io/post/omp-on-high-sierra/ > >> > > > > > > * #5 We should add different omp versions to our benchmarks > >> and > >> > > track > >> > > > > > > the performance, so this data is available for prescribing > >> the best > >> > > > > > > build options and for binary releases. > >> > > > > > > > >> > > > > > > This is also an interesting related gh issue posted in the > >> mkl-dnn > >> > > > > > > repository: https://github.com/intel/mkl-dnn/issues/230 > >> > > > > > > > >> > > > > > > > >> > > > > > > I don't observe the order of magnitude divergence reported by > >> > > Chris in > >> > > > > > > vanilla Ubuntu 18.04 in samples / s but the full training > >> finishes > >> > > > > > > indeed faster with the OMP from 3rdparty (LLVM openmp) vs > >> libgomp. > >> > > > > > > > >> > > > > > > There's also differences in training time when using MKL and > >> the , > >> > > > > > > it's actually a bit slower, I don't know if it's related to > >> OMP. > >> > > > > > > > >> > > > > > > gcc version 7.4.0 (Ubuntu 7.4.0-1ubuntu1~18.04.1) > >> > > > > > > > >> > > > > > > Anton's branch: [email protected]:lebeg/incubator-mxnet.git > >> branch > >> > > > > 'omp' > >> > > > > > > (py3_venv) piotr@ec2 cpu:0: ~/mxnet_openmp [omp]> ldd > >> > > > > > > build/libmxnet.so |grep -i omp > >> > > > > > > libgomp.so.1 => /usr/lib/x86_64-linux-gnu/libgomp.so.1 > >> > > > > > > (0x00007fd99a51d000) > >> > > > > > > > >> > > > > > > time python train_mnist.py > >> > > > > > > > >> > > > > > > INFO:root:Epoch[18] Validation-accuracy=0.984176 > >> > > > > > > INFO:root:Epoch[19] Batch [0-100] Speed: 41617.00 > >> samples/sec > >> > > > > > > accuracy=1.000000 > >> > > > > > > INFO:root:Epoch[19] Batch [100-200] Speed: 47990.69 > >> samples/sec > >> > > > > > > accuracy=0.999531 > >> > > > > > > INFO:root:Epoch[19] Batch [200-300] Speed: 47517.01 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [300-400] Speed: 47430.53 > >> samples/sec > >> > > > > > > accuracy=1.000000 > >> > > > > > > INFO:root:Epoch[19] Batch [400-500] Speed: 47649.77 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [500-600] Speed: 51708.12 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [600-700] Speed: 57228.63 > >> samples/sec > >> > > > > > > accuracy=0.999375 > >> > > > > > > INFO:root:Epoch[19] Batch [700-800] Speed: 50887.85 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [800-900] Speed: 53947.98 > >> samples/sec > >> > > > > > > accuracy=0.999531 > >> > > > > > > INFO:root:Epoch[19] Train-accuracy=0.999717 > >> > > > > > > INFO:root:Epoch[19] Time cost=1.219 > >> > > > > > > INFO:root:Epoch[19] Validation-accuracy=0.983977 > >> > > > > > > 1011.98user 26.78system 0:31.54elapsed 3292%CPU > >> (0avgtext+0avgdata > >> > > > > > > 1146052maxresident)k > >> > > > > > > 0inputs+0outputs (0major+3496364minor)pagefaults 0swaps > >> > > > > > > > >> > > > > > > Master, MKL ON: > >> > > > > > > > >> > > > > > > (py3_venv) piotr@ec2 cpu:1: ~/m/e/image-classification > >> [master]> > >> > > ldd > >> > > > > > > ../../build/libmxnet.so | grep -i omp > >> > > > > > > libomp.so => > >> > > > > > > > >> > > /home/piotr/mxnet_master/build/3rdparty/openmp/runtime/src/libomp.so > >> > > > > > > (0x00007f05ba38f000) > >> > > > > > > libiomp5.so => > >> > > > > > > > >> > > > > > > > >> > > > > > >> > > > >> /home/piotr/mxnet_master/build/mklml/mklml_lnx_2019.0.5.20190502/lib/libiomp5.so > >> > > > > > > (0x00007f05b09f4000) > >> > > > > > > > >> > > > > > > INFO:root:Epoch[18] Validation-accuracy=0.982484 > >> > > > > > > INFO:root:Epoch[19] Batch [0-100] Speed: 36651.63 > >> samples/sec > >> > > > > > > accuracy=0.999691 > >> > > > > > > INFO:root:Epoch[19] Batch [100-200] Speed: 45093.98 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [200-300] Speed: 45146.84 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [300-400] Speed: 45119.90 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [400-500] Speed: 44998.96 > >> samples/sec > >> > > > > > > accuracy=0.999531 > >> > > > > > > INFO:root:Epoch[19] Batch [500-600] Speed: 45072.25 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [600-700] Speed: 44969.79 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [700-800] Speed: 44962.78 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [800-900] Speed: 44945.47 > >> samples/sec > >> > > > > > > accuracy=0.999375 > >> > > > > > > INFO:root:Epoch[19] Train-accuracy=0.999717 > >> > > > > > > INFO:root:Epoch[19] Time cost=1.367 > >> > > > > > > INFO:root:Epoch[19] Validation-accuracy=0.982783 > >> > > > > > > 854.97user 847.21system 0:41.44elapsed 4106%CPU > >> (0avgtext+0avgdata > >> > > > > > > 1154348maxresident)k > >> > > > > > > 0inputs+0outputs (0major+3624361minor)pagefaults 0swaps > >> > > > > > > > >> > > > > > > > >> > > > > > > MKL OFF: > >> > > > > > > (py3_venv) piotr@ec2 cpu:0: ~/mxnet_master [master]> grep -i > >> MKL > >> > > > > > > cmake_options.yml > >> > > > > > > USE_MKL_IF_AVAILABLE: "OFF" # Use MKL if found > >> > > > > > > USE_MKLML_MKL: "OFF" # Use MKLDNN variant of MKL (if MKL > >> found) IF > >> > > > > > > USE_MKL_IF_AVAILABLE AND (NOT APPLE) > >> > > > > > > USE_MKLDNN: "OFF" # Use MKLDNN variant of MKL (if MKL found) > >> IF > >> > > > > > > USE_MKL_IF_AVAILABLE AND (NOT APPLE) > >> > > > > > > (py3_venv) piotr@ec2 cpu:0: ~/mxnet_master [master]> ldd > >> > > > > > > build/libmxnet.so |grep -i omp > >> > > > > > > libomp.so => > >> > > > > > > > >> > > /home/piotr/mxnet_master/build/3rdparty/openmp/runtime/src/libomp.so > >> > > > > > > (0x00007fb720c54000) > >> > > > > > > > >> > > > > > > INFO:root:Epoch[18] Validation-accuracy=0.983479 > >> > > > > > > INFO:root:Epoch[19] Batch [0-100] Speed: 46784.02 > >> samples/sec > >> > > > > > > accuracy=1.000000 > >> > > > > > > INFO:root:Epoch[19] Batch [100-200] Speed: 48824.29 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [200-300] Speed: 49190.31 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [300-400] Speed: 51518.77 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [400-500] Speed: 51551.62 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [500-600] Speed: 49026.35 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Batch [600-700] Speed: 49002.46 > >> samples/sec > >> > > > > > > accuracy=0.999375 > >> > > > > > > INFO:root:Epoch[19] Batch [700-800] Speed: 48980.55 > >> samples/sec > >> > > > > > > accuracy=0.999687 > >> > > > > > > INFO:root:Epoch[19] Batch [800-900] Speed: 47402.56 > >> samples/sec > >> > > > > > > accuracy=0.999844 > >> > > > > > > INFO:root:Epoch[19] Train-accuracy=0.999767 > >> > > > > > > INFO:root:Epoch[19] Time cost=1.259 > >> > > > > > > INFO:root:Epoch[19] Validation-accuracy=0.983181 > >> > > > > > > 755.36user 754.94system 0:35.89elapsed 4207%CPU > >> (0avgtext+0avgdata > >> > > > > > > 1147008maxresident)k > >> > > > > > > 0inputs+3112outputs (0major+3568826minor)pagefaults 0swaps > >> > > > > > > > >> > > > > > > Let me know what you think. > >> > > > > > > > >> > > > > > > Link to the original PR: > >> > > > > > > https://github.com/apache/incubator-mxnet/pull/12160 > >> > > > > > > > >> > > > > > > Thanks. > >> > > > > > > > >> > > > > > > On Wed, Jun 19, 2019 at 5:35 PM kellen sunderland > >> > > > > > > <[email protected]> wrote: > >> > > > > > > > > >> > > > > > > > "if you’re linking in two then you’re doing something > >> wrong." > >> > > > > Correct, > >> > > > > > > > that's one thing I believe we've got consensus on. So > >> let's call > >> > > > > that > >> > > > > > > out > >> > > > > > > > as a bug to be fixed. > >> > > > > > > > > >> > > > > > > > Let's move forward with some reproducible numbers and then > >> > > discuss > >> > > > > the > >> > > > > > > pros > >> > > > > > > > / cons of which particular OMP implementation we should use. > >> > > > > > > > > >> > > > > > > > On Wed, Jun 19, 2019 at 3:06 PM Pedro Larroy < > >> > > > > > > [email protected]> > >> > > > > > > > wrote: > >> > > > > > > > > >> > > > > > > > > Hi Chris > >> > > > > > > > > > >> > > > > > > > > I would ask you to have a bit of patience and help us > >> with your > >> > > > > > > > > experience in this matter. Nobody is ignoring anything, I > >> > > think we > >> > > > > are > >> > > > > > > > > individually gathering feedbacks and trying to understand > >> the > >> > > > > multiple > >> > > > > > > > > contributions done to this topic including yours, then go > >> step > >> > > by > >> > > > > > > > > step, understand what is going on and run experiments and > >> > > report > >> > > > > back > >> > > > > > > > > to the list or the corresponding github item. It was > >> suggested > >> > > by > >> > > > > > > > > Kellen to prepare some containers, this takes effort. > >> > > > > > > > > > >> > > > > > > > > Regarding your final comment, most of us also have many > >> other > >> > > > > things > >> > > > > > > > > to do and responsibilities even if our daytime jobs might > >> > > involve > >> > > > > > > > > MXNet in some form or another. I think that's part of the > >> > > privilege > >> > > > > > > > > and responsibility of working close with an open source > >> > > project and > >> > > > > > > > > the magic of collaboration across organizations. Let's > >> all be > >> > > > > patient > >> > > > > > > > > and take some time to understand and reason about this > >> topic > >> > > which > >> > > > > is > >> > > > > > > > > not simple. Since we decided to step back and gather more > >> data > >> > > > > let's > >> > > > > > > > > take time and do it properly. > >> > > > > > > > > > >> > > > > > > > > Personally I hope to find time to look again into this > >> issue > >> > > before > >> > > > > > > > > the end of the week. > >> > > > > > > > > > >> > > > > > > > > Thanks. > >> > > > > > > > > > >> > > > > > > > > Pedro. > >> > > > > > > > > > >> > > > > > > > > On Wed, Jun 19, 2019 at 2:43 PM Chris Olivier < > >> > > > > [email protected]> > >> > > > > > > > > wrote: > >> > > > > > > > > > > >> > > > > > > > > > if you’re linking in two then you’re doing something > >> wrong. > >> > > You > >> > > > > can > >> > > > > > > see > >> > > > > > > > > by > >> > > > > > > > > > my email yesterday that only one is linked in. This is > >> also > >> > > the > >> > > > > case > >> > > > > > > with > >> > > > > > > > > > the mkl version built by the Makefile — only the Intel > >> OMP > >> > > > > library is > >> > > > > > > > > used > >> > > > > > > > > > (no libgomp). > >> > > > > > > > > > > >> > > > > > > > > > That being said, Do you have clear evidence that using > >> Intel > >> > > OMP > >> > > > > is > >> > > > > > > both > >> > > > > > > > > > problematic and the situation isn’t fixable? The > >> burden of > >> > > > > proof is > >> > > > > > > on > >> > > > > > > > > the > >> > > > > > > > > > ones requesting the change — it is not my > >> responsibility to > >> > > > > justify > >> > > > > > > the > >> > > > > > > > > > current state. There must be something “terrible” and > >> > > unfixable > >> > > > > to > >> > > > > > > > > justify > >> > > > > > > > > > a change. I have seen no proof of this in all this > >> time. > >> > > > > > > > > > > >> > > > > > > > > > On a side note, I mentioned a couple of things in my > >> email > >> > > > > yesterday > >> > > > > > > that > >> > > > > > > > > > still are not being responded to (they were also > >> ignored in > >> > > the > >> > > > > last > >> > > > > > > > > > incarnation of this “discussion” — I have much > >> experience in > >> > > this > >> > > > > > > matter > >> > > > > > > > > to > >> > > > > > > > > > assume “discussion” is a waste of my time, seeing and I > >> am > >> > > not > >> > > > > paid > >> > > > > > > to > >> > > > > > > > > > “work on” mxnet like y’all are). > >> > > > > > > > > > > >> > > > > > > > > > -C > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > On Wed, Jun 19, 2019 at 10:28 AM kellen sunderland < > >> > > > > > > > > > [email protected]> wrote: > >> > > > > > > > > > > >> > > > > > > > > > > I've also quite often seen two versions of OpenMP > >> linked. > >> > > I > >> > > > > think > >> > > > > > > we > >> > > > > > > > > can > >> > > > > > > > > > > all agree we probably want to avoid linking in two > >> > > libraries > >> > > > > that > >> > > > > > > do > >> > > > > > > > > > > effectively the same thing. > >> > > > > > > > > > > > >> > > > > > > > > > > The performance questions should be fairly straight > >> > > forward to > >> > > > > > > > > demonstrate > >> > > > > > > > > > > right? Could we just collaborate on a few minimal > >> > > Dockerfiles > >> > > > > that > >> > > > > > > > > show > >> > > > > > > > > > > (or don't show) Intel OpenMP performance speedups > >> with the > >> > > > > > > workloads > >> > > > > > > > > Chris > >> > > > > > > > > > > is referencing? > >> > > > > > > > > > > > >> > > > > > > > > > > On Wed, Jun 19, 2019 at 4:44 AM Tsukrov, Stanislav < > >> > > > > > > > > > > [email protected]> wrote: > >> > > > > > > > > > > > >> > > > > > > > > > > > Hi, Chris! > >> > > > > > > > > > > > > >> > > > > > > > > > > > Stas here - I've gathered that performance data. > >> > > > > > > > > > > > Sure thing, I can be wrong, but please elaborate a > >> bit on > >> > > > > what > >> > > > > > > we are > >> > > > > > > > > > > > missing. > >> > > > > > > > > > > > Be assured, intentional misdirection was never a > >> case. > >> > > > > > > > > > > > > >> > > > > > > > > > > > Thanks a lot for being constructive. > >> > > > > > > > > > > > > >> > > > > > > > > > > > > Turning Intel OMP on and off (and MKL as well, > >> since it > >> > > > > tends > >> > > > > > > to > >> > > > > > > > > pull > >> > > > > > > > > > > in > >> > > > > > > > > > > > omp, depending which one is linked in). > >> > > > > > > > > > > > > >> > > > > > > > > > > > We never ever considered turning MKL off. We are on > >> the > >> > > same > >> > > > > page > >> > > > > > > > > here - > >> > > > > > > > > > > > MKL is crucial for the performance. > >> > > > > > > > > > > > Why should we? There's a GOMP-linked version of MKL, > >> > > that we > >> > > > > can > >> > > > > > > use. > >> > > > > > > > > > > > > >> > > > > > > > > > > > What we did - we measured, if using compilers > >> default > >> > > OpenMP > >> > > > > > > > > > > > implementation instead of referenced source code > >> > > > > distribution of > >> > > > > > > > > OpenMP > >> > > > > > > > > > > > makes anything slower. > >> > > > > > > > > > > > We have found the impact to be hardly measurable. > >> > > > > > > > > > > > The difference between GOMP and iOMP is <5% on our > >> > > > > benchmarks, > >> > > > > > > most > >> > > > > > > > > of > >> > > > > > > > > > > the > >> > > > > > > > > > > > time less than that. > >> > > > > > > > > > > > > >> > > > > > > > > > > > We just suggest to simplify the build of mxnet, by > >> > > removing > >> > > > > the > >> > > > > > > > > > > > unnecessary dependency. > >> > > > > > > > > > > > > >> > > > > > > > > > > > During that we discovered for example the following > >> > > amazing > >> > > > > > > issue: > >> > > > > > > > > > > > > >> https://github.com/apache/incubator-mxnet/issues/14087 > >> > > > > > > > > > > > > >> > > > > > > > > > > > Best Regards > >> > > > > > > > > > > > > >> > > > > > > > > > > > Stas > >> > > > > > > > > > > > > >> > > > > > > > > > > > On 18.06.19, 18:24, "Chris Olivier" < > >> > > [email protected]> > >> > > > > > > wrote: > >> > > > > > > > > > > > > >> > > > > > > > > > > > I am very reluctant to feed the trolls again, > >> and > >> > > this > >> > > > > will > >> > > > > > > be > >> > > > > > > > > teh > >> > > > > > > > > > > last > >> > > > > > > > > > > > time I address Pedro or Anton on the subject, > >> but > >> > > since I > >> > > > > > > think > >> > > > > > > > > the > >> > > > > > > > > > > > numbers > >> > > > > > > > > > > > being presented are incorrect (either by te > >> builders > >> > > not > >> > > > > > > really > >> > > > > > > > > > > > understanding what they are building, or > >> possibly > >> > > > > intentional > >> > > > > > > > > > > > misdirection): > >> > > > > > > > > > > > > >> > > > > > > > > > > > Turning Intel OMP on and off (and MKL as well, > >> since > >> > > it > >> > > > > > > tends to > >> > > > > > > > > pull > >> > > > > > > > > > > > in > >> > > > > > > > > > > > omp, depending which one is linked in). > >> > > > > > > > > > > > There is a HUGE difference. This is consistent > >> with > >> > > my > >> > > > > > > > > experience > >> > > > > > > > > > > > before > >> > > > > > > > > > > > when it was added. > >> > > > > > > > > > > > > >> > > > > > > > > > > > > >> > > > > > > > > > > > default mnist: > >> > > > > > > > > > > > > >> > > > > > > > > > > > python > >> ../example/image-classification/train_mnist.py > >> > > > > > > > > > > > INFO:root:start with arguments > >> > > Namespace(add_stn=False, > >> > > > > > > > > > > batch_size=64, > >> > > > > > > > > > > > disp_batches=100, dtype='float32', > >> gc_threshold=0.5, > >> > > > > > > > > gc_type='none', > >> > > > > > > > > > > > gpus=None, image_shape='1, 28, 28', > >> > > > > initializer='default', > >> > > > > > > > > > > > kv_store='device', load_epoch=None, loss='', > >> lr=0.05, > >> > > > > > > > > lr_factor=0.1, > >> > > > > > > > > > > > lr_step_epochs='10', macrobatch_size=0, > >> > > > > model_prefix=None, > >> > > > > > > > > mom=0.9, > >> > > > > > > > > > > > monitor=0, network='mlp', num_classes=10, > >> > > num_epochs=20, > >> > > > > > > > > > > > num_examples=60000, num_layers=None, > >> optimizer='sgd', > >> > > > > > > > > > > > profile_server_suffix='', > >> profile_worker_suffix='', > >> > > > > > > > > save_period=1, > >> > > > > > > > > > > > test_io=0, top_k=0, warmup_epochs=5, > >> > > > > > > warmup_strategy='linear', > >> > > > > > > > > > > > wd=0.0001) > >> > > > > > > > > > > > > >> > > > > > > > > > > > INTEL OMP: > >> > > > > > > > > > > > > >> > > > > > > > > > > > ldd libmxnet.so | grep omp > >> > > > > > > > > > > > libomp.so => > >> > > > > > > > > > > > > >> > > > > > > > > > >> > > > > > >> /home/chris/src/mxnet/cmake_omp/3rdparty/openmp/runtime/src/libomp.so > >> > > > > > > > > > > > (0x00007f978fde7000) > >> > > > > > > > > > > > > >> > > > > > > > > > > > :root:Epoch[0] Batch [0-100] Speed: > >> 31548.09 > >> > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.780012 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [100-200] Speed: > >> > > 16073.21 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.920469 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [200-300] Speed: > >> > > 19075.91 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.928281 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [300-400] Speed: > >> > > 23211.36 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.942813 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [400-500] Speed: > >> > > 22139.79 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.938750 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [500-600] Speed: > >> > > 23225.52 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.946562 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [600-700] Speed: > >> > > 19547.41 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.953281 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [700-800] Speed: > >> > > 24111.73 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.951562 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [800-900] Speed: > >> > > 13959.88 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.957500 > >> > > > > > > > > > > > INFO:root:Epoch[0] Train-accuracy=0.925423 > >> > > > > > > > > > > > INFO:root:Epoch[0] Time cost=3.806 > >> > > > > > > > > > > > INFO:root:Epoch[0] Validation-accuracy=0.962580 > >> > > > > > > > > > > > INFO:root:Epoch[1] Batch [0-100] Speed: > >> > > 24560.21 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.968131 > >> > > > > > > > > > > > INFO:root:Epoch[1] Batch [100-200] Speed: > >> > > 23457.03 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.966250 > >> > > > > > > > > > > > > >> > > > > > > > > > > > > >> > > > > > > > > > > > LIBGOMP: > >> > > > > > > > > > > > > >> > > > > > > > > > > > ldd libmxnet.so | grep omp > >> > > > > > > > > > > > libgomp.so.1 => > >> > > > > > > /usr/lib/x86_64-linux-gnu/libgomp.so.1 > >> > > > > > > > > > > > (0x00007f25c25dd000) > >> > > > > > > > > > > > > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [0-100] Speed: > >> > > 1731.01 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.782488 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [100-200] Speed: > >> > > 3551.32 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.907813 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [200-300] Speed: > >> > > 1991.00 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.927188 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [300-400] Speed: > >> > > 2175.45 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.937969 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [400-500] Speed: > >> > > 1644.95 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.942187 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [500-600] Speed: > >> > > 6444.58 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.950156 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [600-700] Speed: > >> > > 7842.16 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.947969 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [700-800] Speed: > >> > > 9412.07 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.953750 > >> > > > > > > > > > > > INFO:root:Epoch[0] Batch [800-900] Speed: > >> > > 12707.58 > >> > > > > > > > > samples/sec > >> > > > > > > > > > > > accuracy=0.953125 > >> > > > > > > > > > > > > >> > > > > > > > > > > > That being said, there's other issued beyond > >> speed. > >> > > The > >> > > > > > > DEFAULT > >> > > > > > > > > > > build > >> > > > > > > > > > > > from > >> > > > > > > > > > > > makefile (not CMake) uses Intel OMP mkl (I > >> showed > >> > > > > before) and > >> > > > > > > > > > > > mysteriously > >> > > > > > > > > > > > it has no issues? This seems highly suspicious. > >> > > All I > >> > > > > see > >> > > > > > > is a > >> > > > > > > > > lot > >> > > > > > > > > > > of > >> > > > > > > > > > > > hand-waving and conjecture and pointing to > >> > > StackOverflow > >> > > > > > > posts > >> > > > > > > > > made > >> > > > > > > > > > > by > >> > > > > > > > > > > > people who may be of questionable pedigree to > >> begin > >> > > with. > >> > > > > > > This > >> > > > > > > > > > > smells > >> > > > > > > > > > > > of a > >> > > > > > > > > > > > Pedro-ego-fight rather than one of purely > >> technical > >> > > > > merit. > >> > > > > > > > > Also, if > >> > > > > > > > > > > > one > >> > > > > > > > > > > > knows how OMP works, they would be very > >> suspicious > >> > > of > >> > > > > the > >> > > > > > > > > > > > "intermittent > >> > > > > > > > > > > > hangs" claim -- that's probably just broken race > >> > > > > conditions > >> > > > > > > > > elsewhere > >> > > > > > > > > > > > until > >> > > > > > > > > > > > proven differently. It'd tend freeze on the > >> first > >> > > use if > >> > > > > > > > > something > >> > > > > > > > > > > is > >> > > > > > > > > > > > wrong (try using libgomp after a fork and see), > >> since > >> > > > > worker > >> > > > > > > > > threads" > >> > > > > > > > > > > > wouldn't be assigned/joined properly. IntelOMP > >> is > >> > > > > faster, > >> > > > > > > but > >> > > > > > > > > also > >> > > > > > > > > > > has > >> > > > > > > > > > > > other advantages, such as allowing OMP after a > >> fork. > >> > > > > > > > > > > > > >> > > > > > > > > > > > I actually addressed a lot of issues and ask for > >> > > > > > > clarification > >> > > > > > > > > in the > >> > > > > > > > > > > > original PR's way back when, but they're all > >> just > >> > > > > ignored. > >> > > > > > > > > > > > > >> > > > > > > > > > > > -Chris > >> > > > > > > > > > > > > >> > > > > > > > > > > > > >> > > > > > > > > > > > > >> > > > > > > > > > > > > >> > > > > > > > > > > > >> > > > > > > > > > >> > > > > > > > >> > > > > > >> > > > >> > >
