OpenMp on Cooley
The OpenMP API is an open standard for parallel programming. The specification document can be found here: https://www.openmp.org. The specification describes directives, runtime routines, and environment variables that allow an application developer to express parallelism (e.g. shared memory multiprocessing and device offloading). Many compiler vendors provide implementations of the OpenMP specification.
Using OpenMP at ALCF
OpenMP support for CPUs on Cooley is provided through the GNU, Intel, and LLVM Clang compilers available on Cooley. Guidance on updating your environment to use one of these compilers is available here.
OpenMP offload support for GPUs on Cooley is provided via community compilers. The LLVM Clang compiler is installed on Cooley to support OpenMP 4.5+ offload features. The compiler is in rapid development and ALCF staff build it frequently from the master branch.
The status of offload features in this compiler is available on the LLVM Clang website. Considering that the compiler is under active development, the compiler may contain bugs and those should be reported directly to the compiler team here.
Building on Cooley
OpenMP parallelism for CPUs can be enabled for each supported compiler using the appropriate compiler flag: -fopenmp for GNU/Clang and -qopenmp for Intel compilers.
OpenMP settings, such as number of threads and affinity, can be controlled via OpenMP environment variables.
The offload compiler is installed on Cooley at /soft/compilers/clang-ykt. To use this compiler, first update your software environment with the following CUDA and gcc softkeys and paths.
The following compiler flags are needed to enable offload compilation:
Running jobs on Cooley
An example ‘test.sh’ job submission script follows.
To request a single node with 10 minutes of walltime, charging to the MyProject project, one can use the following command.
There are a handful of simple examples available in the /soft/compilers/clang-ykt/example directory. To run an example, copy the source file to current working directory, compile, and submit to a compute node in an interactive job or as batch job using example script above.
cp /soft/compilers/clang-ykt/example/test_simple2.cpp ./ clang++ -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda test_simple2.cpp ./a.out enter constructor 0x7ffe3f6bb648 host pointer 0x2125590 device pointer 0x620b840200 Running target region on device! maptest constructor check_size = 6 check_value = 1
The NVIDIA tools can be used to debug and profile offloaded kernels compiled with the OpenMP offload clang-ykt compiler. For example, nvprof can be used to profile and verify that your application offloaded kernels to the GPUs on Cooley.
nvprof ./a.out ==2755== NVPROF is profiling process 2755, command: ./a.out enter constructor 0x7ffea1bb91c8 host pointer 0x3fcc7a0 device pointer 0x620c240200 Running target region on device! maptest constructor check_size = 6 check_value = 1 ==2755== Profiling application: ./a.out ==2755== Profiling result: Type Time(%) Time Calls Avg Min Max Name GPU activities: 48.51% 156.96us 1 156.96us 156.96us 156.96us __omp_offloading_2d_e6fe0d__ZN7maptestIdEC1Em_l18 46.44% 150.27us 1 150.27us 150.27us 150.27us __omp_offloading_2d_e6fe0d__ZN7maptestIdE3runEv_l51 3.56% 11.520us 5 2.3040us 1.9840us 2.7520us [CUDA memcpy DtoH] 1.49% 4.8310us 3 1.6100us 1.3110us 2.2080us [CUDA memcpy HtoD] API calls: 75.94% 292.38ms 1 292.38ms 292.38ms 292.38ms cuCtxCreate 21.48% 82.678ms 1 82.678ms 82.678ms 82.678ms cuCtxDestroy 0.98% 3.7905ms 256 14.806us 1.3690us 458.24us cuStreamCreate 0.69% 2.6478ms 1 2.6478ms 2.6478ms 2.6478ms cuModuleLoadDataEx 0.39% 1.4933ms 1 1.4933ms 1.4933ms 1.4933ms cuModuleUnload 0.17% 638.23us 256 2.4930us 2.0450us 23.244us cuStreamDestroy …