You can run it in the debugger (-start_in_debugger noxterm) and put a break point in MatMult(). Then print the matrix in the debugger (for example p *mat) it will dump a huge amount of stuff including the function pointer table, in that there should be a MatMult_seqaijcusparse function pointer. If it is MatMult_SeqAIJ then that is why it is not running on the GPU. Look at the other functions in the function table, are any of the cuSPARSE functions.
> DIFFERENT_NONZERO_PATTERN should not affect what functions it uses for the > operations Barry This is why running with just 1 MPI rank is so much easier. > On Jan 15, 2022, at 1:03 AM, Rohan Yadav <roh...@alumni.cmu.edu> wrote: > > Ok, I'll try looking with greps like and see what I find. > > > My guess why your code is not using the seqaijcusparse is that you are not > > setting the type before you call MatLoad() hence it loads with SeqAIJ. > > -mat_type does not magically change a type once a matrix has a set type. I > > agree our documentation on how to make objects be GPU objects is horrible > > now. > > I printed out my matrices with the PetscViewer objects and can confirm that > the type is seqaijcusparse. Perhaps for the way I'm using it > (DIFFERENT_NONZERO_PATTERN) the kernel is unsupported? I'm not sure how to > get any more diagnostic info about why the cuda kernel isn't called... > > Rohan > > On Fri, Jan 14, 2022 at 9:46 PM Barry Smith <bsm...@petsc.dev > <mailto:bsm...@petsc.dev>> wrote: > > This changes rapidly and depends on if the backend is CUDA, HIP, Sycl, or > Kokkos. The only way to find out definitively is with, for example, > > git grep MatMult_ | egrep -i "(cusparse|cublas|cuda)" > > > Because of our, unfortunately, earlier naming choices you need to kind of > know what to grep for, for CUDA it may be cuSparse or cuBLAS > > Not yet merged branches may also have some operations that are still being > developed. > > My guess why your code is not using the seqaijcusparse is that you are not > setting the type before you call MatLoad() hence it loads with SeqAIJ. > -mat_type does not magically change a type once a matrix has a set type. I > agree our documentation on how to make objects be GPU objects is horrible now. > > Barry > > >> On Jan 15, 2022, at 12:31 AM, Rohan Yadav <roh...@alumni.cmu.edu >> <mailto:roh...@alumni.cmu.edu>> wrote: >> >> 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 >> <mailto: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 >>> <mailto: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 >>> >>> <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 >>> <mailto: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 >>> <mailto: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 >> >
