Thanks for the great summary, Anton. I'm curious that is there anybody builds mxnet successfully with ICC/ICPC?
-----Original Message----- From: Anton Chernov [mailto:mecher...@gmail.com] Sent: Thursday, November 22, 2018 8:36 PM To: d...@mxnet.apache.org Subject: [Discussion] Remove bundled llvm OpenMP Dear MXNet community, I would like to drive attention to an important issue that is present in the MXNet CMake build: usage of bundled llvm OpenMP library. I have opened a PR to remove it: https://github.com/apache/incubator-mxnet/pull/12160 The issue was closed, but I am strong in my oppinion that it's the right thing to do. *Background* If you want to use OpenMP pragmas in your code for parallelization you would supply a special flag to the compiler: - Clang / -fopenmp https://openmp.llvm.org/ - GCC / -fopenmp https://gcc.gnu.org/onlinedocs/libgomp/Enabling-OpenMP.html - Intel / [Q]openmp https://software.intel.com/en-us/node/522689#6E24682E-F411-4AE3-A04D-ECD81C7008D1 - Visual Studio: /openmp (Enable OpenMP 2.0 Support) https://msdn.microsoft.com/en-us/library/tt15eb9t.aspx Each of the compilers would enable the '#pragma omp' directive during C/C++ compilation and arrange for automatic linking of the OpenMP runtime library supplied by each complier separately. Thus, to use the advantages of an OpenMP implementation one has to compile the code with the corresponding compiler. Currently, in MXNet CMake build scripts a bundled version of llvm OpenMP is used ([1] and [2]) to replace the OpenMP library supplied by the compiler. I will quote here the README from the MKL-DNN (Intel(R) Math Kernel Library for Deep Neural Networks): "Intel MKL-DNN uses OpenMP* for parallelism and requires an OpenMP runtime library to work. As different OpenMP runtimes may not be binary compatible it's important to ensure that only one OpenMP runtime is used throughout the application. Having more than one OpenMP runtime initialized may lead to undefined behavior resulting in incorrect results or crashes." [3] And: "Using GNU compiler with -fopenmp and -liomp5 options will link the application with both Intel and GNU OpenMP runtime libraries. This will lead to undefined behavior of the application." [4] As can be seen from ldd for MXNet: $ ldd build/tests/mxnet_unit_tests | grep omp libomp.so => /.../mxnet/build/3rdparty/openmp/runtime/src/libomp.so (0x00007f697bc55000) libgomp.so.1 => /usr/lib/x86_64-linux-gnu/libgomp.so.1 (0x00007f69660cd000) *Performance* The only performance data related to OpenMP in MXNet I was able to find is here: https://github.com/apache/incubator-mxnet/issues/9744#issuecomment-367711172 Which in my understanding is testing imact of different environment variables for the same setup (using same bundled OpenMP library). The libraries may differ in implementation and the Thread Affinity Interface [5] may have significant impact on performance. All compliers support it: - Clang / KMP_AFFINITY https://github.com/clang-ykt/openmp/blob/master/runtime/src/kmp_affinity.cpp - GCC / GOMP_CPU_AFFINITY https://gcc.gnu.org/onlinedocs/gcc-4.7.1/libgomp/GOMP_005fCPU_005fAFFINITY.html - Intel / KMP_AFFINITY https://software.intel.com/en-us/node/522689#6E24682E-F411-4AE3-A04D-ECD81C7008D1 - Visual Studio / SetThreadAffinityMask https://docs.microsoft.com/en-us/windows/desktop/api/winbase/nf-winbase-setthreadaffinitymask *Issues* Failed OpenMP assertion when loading MXNet compiled with DEBUG=1 https://github.com/apache/incubator-mxnet/issues/10856 libomp.so dependency (need REAL fix) https://github.com/apache/incubator-mxnet/issues/11417 mxnet-mkl (v0.12.0) crash when using (conda-installed) numpy with MKL https://github.com/apache/incubator-mxnet/issues/8532 Performance regression when OMP_NUM_THREADS environment variable is not set https://github.com/apache/incubator-mxnet/issues/9744 Poor concat CPU performance on CUDA builds https://github.com/apache/incubator-mxnet/issues/11905 I would appreciate hearing your thoughts. Best Anton [1] https://github.com/apache/incubator-mxnet/blob/master/CMakeLists.txt#L400-L405 [2] https://github.com/apache/incubator-mxnet/tree/master/3rdparty [3] https://github.com/intel/mkl-dnn/blame/master/README.md#L261-L265 [4] https://github.com/intel/mkl-dnn/blame/master/README.md#L278-L280 [5] https://software.intel.com/en-us/node/522691
