[petsc-dev] Fwd: Using PETSC with GPUs
Rohan Yadav
rohany at alumni.cmu.edu
Sat Jan 15 00:16:30 CST 2022
---------- Forwarded message ---------
From: Rohan Yadav <rohany at alumni.cmu.edu>
Date: Fri, Jan 14, 2022 at 10:03 PM
Subject: Re: [petsc-dev] Using PETSC with GPUs
To: Barry Smith <bsmith at petsc.dev>
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 <bsmith at 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 <rohany at 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 <bsmith at 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 <rohany at 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 <mfadams at 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 <rohany at 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
>>>>
>>>
>>
>
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