Do you have any experience with nsparse?
https://github.com/EBD-CREST/nsparse
I've seen claims that it is much faster than cuSPARSE for sparse
matrix-matrix products.
Karl Rupp via petsc-dev writes:
> Hi Richard,
>
> CPU spGEMM is about twice as fast even on the GPU-friendly case of a
>
Hi Richard,
CPU spGEMM is about twice as fast even on the GPU-friendly case of a
single rank: http://viennacl.sourceforge.net/viennacl-benchmarks-spmm.html
I agree that it would be good to have a GPU-MatMatMult for the sake of
experiments. Under these performance constraints it's not top
FWIW, I've heard that CUSPARSE is going to provide integer matrix-matrix
products for indexing applications, and that it should be easy to extend
that to double, etc.
On Wed, Oct 2, 2019 at 6:00 PM Mills, Richard Tran via petsc-dev <
petsc-dev@mcs.anl.gov> wrote:
> Fellow PETSc developers,
>
> I
Fellow PETSc developers,
I am wondering why the AIJCUSPARSE and AIJVIENNACL matrix types do not support
the sparse matrix-matrix multiplication (SpGEMM, or MatMatMult() in PETSc
parlance) routines provided by cuSPARSE and ViennaCL, respectively. Is there a
good reason that I shouldn't add
Mark,
It looks like you are missing some critical CUDA library (or libraries) in your
link line. I know you will at least need the CUDA runtime "-lcudart". Look at
something like PETSC_WITH_EXTERNAL_LIB for one of your CUDA-enabled PETSc
builds in $PETSC_ARCH/lib/petsc/conf/petscvariables to
In MR !2138 I have this target as show-fail which I think is more
descriptive.
config/report_tests.py -f
is what's done directly.
I made it such that one can copy and paste, but it might be too verbose.
Scott
On 9/20/19 8:53 PM, Jed Brown wrote:
"Smith, Barry F." writes:
Satish and
Fixed in MR# 2138
https://gitlab.com/petsc/petsc/merge_requests/2138
Thanks for the report.
Scott
On 9/28/19 3:44 AM, Pierre Jolivet via petsc-dev wrote:
Hello,
If I put something like this in src/ksp/ksp/examples/tutorials/ex12.c
args: -ksp_gmres_cgs_refinement_type refine_always
I found a CUDAVersion.cu of STREAMS and tried to build it. I got it to
compile manually with:
nvcc -o CUDAVersion.o -ccbin pgc++
-I/autofs/nccs-svm1_sw/summit/.swci/1-compute/opt/spack/20180914/linux-rhel7-ppc64le/pgi-19.4/spectrum-mpi-10.3.0.1-20190611-4ymaahbai7ehhw4rves5jjiwon2laz3a/include
Yes, the name valid_GPU_array is very confusing. I read it as valid_places.
--Junchao Zhang
On Wed, Oct 2, 2019 at 1:12 AM Karl Rupp
mailto:r...@iue.tuwien.ac.at>> wrote:
Hi Junchao,
I recall that Jed already suggested to make this a bitmask ~7 years ago ;-)
On the other hand: If we touch
Hi Junchao,
I recall that Jed already suggested to make this a bitmask ~7 years ago ;-)
On the other hand: If we touch valid_GPU_array, then we should also use
a better name or refactor completely. Code like
(V->valid_GPU_array & PETSC_OFFLOAD_GPU)
simply isn't intuitive (nor does it make
10 matches
Mail list logo
