I was wondering if there is a definitive list for what operations are and aren't supported for distributed GPU execution. For some operations, like `MatMult`, it is clear that MPIAIJCUSPARSE implements MatMult from the documentation, but other operations it is unclear, such as MatMatMult. Another scenario is the MatAXPY kernel, which supposedly has a SeqAIJCUSPARSE implementation, which I take means that it can only execute on a single GPU. However, even if I pass -mat_type seqaijcusparse to the kernel it doesn't seem to utilize the GPU.
Rohan On Fri, Jan 14, 2022 at 4:05 PM Barry Smith <bsm...@petsc.dev> wrote: > > Just use 1 MPI rank. > > > ------------------------------------------------------------------------------------------------------------------------ > Event Count Time (sec) Flop > --- Global --- --- Stage ---- Total GPU - CpuToGpu - - > GpuToCpu - GPU > Max Ratio Max Ratio Max Ratio Mess AvgLen > Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s Mflop/s Count Size > Count Size %F > > --------------------------------------------------------------------------------------------------------------------------------------------------------------- > > --- Event Stage 0: Main Stage > > BuildTwoSided 1 1.0 1.8650e-013467.8 0.00e+00 0.0 2.0e+00 4.0e+00 > 1.0e+00 0 0 3 0 2 0 0 3 0 4 0 0 0 0.00e+00 0 > 0.00e+00 0 > MatMult 30 1.0 6.6642e+01 1.0 1.16e+10 1.0 6.4e+01 6.4e+08 > 1.0e+00 65100 91 93 2 65100 91 93 4 346 0 0 0.00e+00 31 > 2.65e+04 0 > > From this it is clear the matrix never ended up on the GPU, but the vector > did. For each multiply, it is copying the vector from the GPU to the CPU > and then doing the MatMult on the CPU. If the MatMult was done on the GPU > the file number in the row would be 100% indicating all the flops were done > on the GPU and the fifth from the end value of 0 would be some large > number, being the flop rate on the GPU. > > > > On Jan 14, 2022, at 4:59 PM, Rohan Yadav <roh...@alumni.cmu.edu> wrote: > > A log_view is attached at the end of the mail. > > I am running on a large problem size (639 million nonzeros). > > > * I assume you are assembling the matrix on the CPU. The copy of data to > the GPU takes time and you really should be creating the matrix on the GPU > > How do I do this? I'm loading the matrix in from a file, but I'm running > the computation several times (and with a warmup), so I would expect that > the data is copied onto the GPU the first time. My (cpu) code to do this is > here: > https://github.com/rohany/taco/blob/5c0a4f4419ba392838590ce24e0043f632409e7b/petsc/benchmark.cpp#L68 > . > > Log view: > > ---------------------------------------------- PETSc Performance Summary: > ---------------------------------------------- > > ./bin/benchmark on a named lassen75 with 2 processors, by yadav2 Fri Jan > 14 13:54:09 2022 > Using Petsc Release Version 3.16.3, unknown > > Max Max/Min Avg Total > Time (sec): 1.026e+02 1.000 1.026e+02 > Objects: 1.200e+01 1.000 1.200e+01 > Flop: 1.156e+10 1.009 1.151e+10 2.303e+10 > Flop/sec: 1.127e+08 1.009 1.122e+08 2.245e+08 > MPI Messages: 3.500e+01 1.000 3.500e+01 7.000e+01 > MPI Message Lengths: 2.210e+10 1.000 6.313e+08 4.419e+10 > MPI Reductions: 4.100e+01 1.000 > > Flop counting convention: 1 flop = 1 real number operation of type > (multiply/divide/add/subtract) > e.g., VecAXPY() for real vectors of length N > --> 2N flop > and VecAXPY() for complex vectors of length N > --> 8N flop > > Summary of Stages: ----- Time ------ ----- Flop ------ --- Messages > --- -- Message Lengths -- -- Reductions -- > Avg %Total Avg %Total Count > %Total Avg %Total Count %Total > 0: Main Stage: 1.0257e+02 100.0% 2.3025e+10 100.0% 7.000e+01 > 100.0% 6.313e+08 100.0% 2.300e+01 56.1% > > > ------------------------------------------------------------------------------------------------------------------------ > See the 'Profiling' chapter of the users' manual for details on > interpreting output. > Phase summary info: > Count: number of times phase was executed > Time and Flop: Max - maximum over all processors > Ratio - ratio of maximum to minimum over all processors > Mess: number of messages sent > AvgLen: average message length (bytes) > Reduct: number of global reductions > Global: entire computation > Stage: stages of a computation. Set stages with PetscLogStagePush() and > PetscLogStagePop(). > %T - percent time in this phase %F - percent flop in this > phase > %M - percent messages in this phase %L - percent message lengths > in this phase > %R - percent reductions in this phase > Total Mflop/s: 10e-6 * (sum of flop over all processors)/(max time over > all processors) > GPU Mflop/s: 10e-6 * (sum of flop on GPU over all processors)/(max GPU > time over all processors) > CpuToGpu Count: total number of CPU to GPU copies per processor > CpuToGpu Size (Mbytes): 10e-6 * (total size of CPU to GPU copies per > processor) > GpuToCpu Count: total number of GPU to CPU copies per processor > GpuToCpu Size (Mbytes): 10e-6 * (total size of GPU to CPU copies per > processor) > GPU %F: percent flops on GPU in this event > > ------------------------------------------------------------------------------------------------------------------------ > Event Count Time (sec) Flop > --- Global --- --- Stage ---- Total GPU - CpuToGpu - - > GpuToCpu - GPU > Max Ratio Max Ratio Max Ratio Mess AvgLen > Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s Mflop/s Count Size > Count Size %F > > --------------------------------------------------------------------------------------------------------------------------------------------------------------- > > --- Event Stage 0: Main Stage > > BuildTwoSided 1 1.0 1.8650e-013467.8 0.00e+00 0.0 2.0e+00 4.0e+00 > 1.0e+00 0 0 3 0 2 0 0 3 0 4 0 0 0 0.00e+00 0 > 0.00e+00 0 > MatMult 30 1.0 6.6642e+01 1.0 1.16e+10 1.0 6.4e+01 6.4e+08 > 1.0e+00 65100 91 93 2 65100 91 93 4 346 0 0 0.00e+00 31 > 2.65e+04 0 > MatAssemblyBegin 1 1.0 3.1100e-07 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > MatAssemblyEnd 1 1.0 1.9798e+01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 4.0e+00 19 0 0 0 10 19 0 0 0 17 0 0 0 0.00e+00 0 > 0.00e+00 0 > MatLoad 1 1.0 3.5519e+01 1.0 0.00e+00 0.0 6.0e+00 5.4e+08 > 1.6e+01 35 0 9 7 39 35 0 9 7 70 0 0 0 0.00e+00 0 > 0.00e+00 0 > VecSet 5 1.0 5.8959e-02 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > VecScatterBegin 30 1.0 5.4085e+00 1.0 0.00e+00 0.0 6.4e+01 6.4e+08 > 1.0e+00 5 0 91 93 2 5 0 91 93 4 0 0 0 0.00e+00 0 > 0.00e+00 0 > VecScatterEnd 30 1.0 9.2544e+00 2.5 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 6 0 0 0 0 6 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > VecCUDACopyFrom 31 1.0 4.0174e-01 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 31 > 2.65e+04 0 > SFSetGraph 1 1.0 4.4912e-02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > SFSetUp 1 1.0 5.2595e+00 1.0 0.00e+00 0.0 4.0e+00 1.7e+08 > 1.0e+00 5 0 6 2 2 5 0 6 2 4 0 0 0 0.00e+00 0 > 0.00e+00 0 > SFPack 30 1.0 3.4021e-02 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > SFUnpack 30 1.0 1.9222e-05 1.5 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00e+00 0 > 0.00e+00 0 > > --------------------------------------------------------------------------------------------------------------------------------------------------------------- > > Memory usage is given in bytes: > > Object Type Creations Destructions Memory Descendants' Mem. > Reports information only for process 0. > > --- Event Stage 0: Main Stage > > Matrix 3 0 0 0. > Viewer 2 0 0 0. > Vector 4 1 1792 0. > Index Set 2 2 335250404 0. > Star Forest Graph 1 0 0 0. > > ======================================================================================================================== > Average time to get PetscTime(): 3.77e-08 > Average time for MPI_Barrier(): 8.754e-07 > Average time for zero size MPI_Send(): 2.6755e-06 > #PETSc Option Table entries: > -log_view > -mat_type aijcusparse > -matrix /p/gpfs1/yadav2/tensors//petsc/kmer_V1r.petsc > -n 20 > -vec_type cuda > -warmup 10 > #End of PETSc Option Table entries > Compiled without FORTRAN kernels > Compiled with full precision matrices (default) > sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8 > sizeof(PetscScalar) 8 sizeof(PetscInt) 4 > Configure options: --download-c2html=0 --download-hwloc=0 > --download-sowing=0 --prefix=./petsc-install/ --with-64-bit-indices=0 > --with-blaslapack-lib="/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib/liblapack.so > /usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib/libblas.so" > --with-cc=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc > --with-clanguage=C --with-cxx-dialect=C++17 > --with-cxx=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpig++ > --with-cuda=1 --with-debugging=0 > --with-fc=/usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran > --with-fftw=0 > --with-hdf5-dir=/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4 > --with-hdf5=1 --with-mumps=0 --with-precision=double --with-scalapack=0 > --with-scalar-type=real --with-shared-libraries=1 --with-ssl=0 > --with-suitesparse=0 --with-trilinos=0 --with-valgrind=0 --with-x=0 > --with-zlib-include=/usr/include --with-zlib-lib=/usr/lib64/libz.so > --with-zlib=1 CFLAGS="-g -DNoChange" COPTFLAGS="-O3" CXXFLAGS="-O3" > CXXOPTFLAGS="-O3" FFLAGS=-g CUDAFLAGS=-std=c++17 FOPTFLAGS= > PETSC_ARCH=arch-linux-c-opt > ----------------------------------------- > Libraries compiled on 2022-01-14 20:56:04 on lassen99 > Machine characteristics: > Linux-4.14.0-115.21.2.1chaos.ch6a.ppc64le-ppc64le-with-redhat-7.6-Maipo > Using PETSc directory: /g/g15/yadav2/taco/petsc/petsc/petsc-install > Using PETSc arch: > ----------------------------------------- > > Using C compiler: > /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc > -g -DNoChange -fPIC "-O3" > Using Fortran compiler: > /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran > -g -fPIC > ----------------------------------------- > > Using include paths: > -I/g/g15/yadav2/taco/petsc/petsc/petsc-install/include > -I/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/include > -I/usr/include -I/usr/tce/packages/cuda/cuda-11.1.0/include > ----------------------------------------- > > Using C linker: > /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigcc > Using Fortran linker: > /usr/tce/packages/spectrum-mpi/spectrum-mpi-rolling-release-gcc-8.3.1/bin/mpigfortran > Using libraries: > -Wl,-rpath,/g/g15/yadav2/taco/petsc/petsc/petsc-install/lib > -L/g/g15/yadav2/taco/petsc/petsc/petsc-install/lib -lpetsc > -Wl,-rpath,/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib > -L/usr/tcetmp/packages/lapack/lapack-3.9.0-gcc-7.3.1/lib > -Wl,-rpath,/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/lib > -L/usr/tcetmp/packages/petsc/build/3.13.0/spack/opt/spack/linux-rhel7-power9le/xl_r-16.1/hdf5-1.10.6-e7e7urb5k7va3ib7j4uro56grvzmcmd4/lib > -Wl,-rpath,/usr/tce/packages/cuda/cuda-11.1.0/lib64 > -L/usr/tce/packages/cuda/cuda-11.1.0/lib64 > -Wl,-rpath,/usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib > -L/usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib > -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc/ppc64le-redhat-linux/8 > -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc/ppc64le-redhat-linux/8 > -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc > -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib/gcc > -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib64 > -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib64 > -Wl,-rpath,/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib > -L/usr/tce/packages/gcc/gcc-8.3.1/rh/usr/lib -llapack -lblas -lhdf5_hl > -lhdf5 -lm /usr/lib64/libz.so -lcuda -lcudart -lcufft -lcublas -lcusparse > -lcusolver -lcurand -lstdc++ -ldl -lmpiprofilesupport -lmpi_ibm_usempi > -lmpi_ibm_mpifh -lmpi_ibm -lgfortran -lm -lgfortran -lm -lgcc_s -lquadmath > -lpthread -lquadmath -lstdc++ -ldl > ----------------------------------------- > > On Fri, Jan 14, 2022 at 1:43 PM Mark Adams <mfad...@lbl.gov> wrote: > >> There are a few things: >> * GPU have higher latencies and so you basically need a large >> enough problem to get GPU speedup >> * I assume you are assembling the matrix on the CPU. The copy of data to >> the GPU takes time and you really should be creating the matrix on the GPU >> * I agree with Barry, Roughly 1M / GPU is around where you start seeing a >> win but this depends on a lot of things. >> * There are startup costs, like the CPU-GPU copy. It is best to run one >> mat-vec, or whatever, push a new stage and then run the benchmark. The >> timing for this new stage will be separate in the log view data. Look at >> that. >> - You can fake this by running your benchmark many times to amortize any >> setup costs. >> >> On Fri, Jan 14, 2022 at 4:27 PM Rohan Yadav <roh...@alumni.cmu.edu> >> wrote: >> >>> Hi, >>> >>> I'm looking to use PETSc with GPUs to do some linear algebra operations, >>> like SpMV, SPMM etc. Building PETSc with `--with-cuda=1` and running with >>> `-mat_type aijcusparse -vec_type cuda` gives me a large slowdown from the >>> same code running on the CPU. This is not entirely unexpected, as things >>> like data transfer costs across the PCIE might erroneously be included in >>> my timing. Are there some examples of benchmarking GPU computations with >>> PETSc, or just the proper way to write code in PETSc that will work for >>> CPUs and GPUs? >>> >>> Rohan >>> >> >