I would like a mode for the PETSc matrix classes where the values are simply
shipped to and used on the GPU in single precision. In theory it is trivial to
implement.
> On Jul 11, 2019, at 3:31 PM, Jed Brown via petsc-dev
> wrote:
>
> "Zhang, Junchao" writes:
>
>> A side question: Do l
"Zhang, Junchao" writes:
> A side question: Do lossy compressors have value for PETSc?
Perhaps if they're very fast, but I think it's usually not PETSc's place
to be performing such compression due to tolerances being really subtle.
There certainly is a place for preconditioning using reduced p
A side question: Do lossy compressors have value for PETSc?
--Junchao Zhang
On Thu, Jul 11, 2019 at 9:06 AM Jed Brown via petsc-dev
mailto:petsc-dev@mcs.anl.gov>> wrote:
Zstd is a remarkably good compressor. I've experimented with it for
compressing column indices for sparse matrices on structu
"Smith, Barry F." writes:
> Sorry, I wasn't clear. Just meant something simpler. Compress the matrix to
> copy it to the GPU for faster transfers (and uncompress it appropriately on
> the GPU).
Oh, perhaps. Probably not relevant with NVLink (because it's nearly as fast as
DRAM), but could
Sorry, I wasn't clear. Just meant something simpler. Compress the matrix to
copy it to the GPU for faster transfers (and uncompress it appropriately on the
GPU).
Barry
> On Jul 11, 2019, at 10:49 AM, Jed Brown wrote:
>
> I don't know anything about zstd (or competitive compression) fo
I don't know anything about zstd (or competitive compression) for GPU,
but doubt it works at the desired granularity. I think SpMV on late-gen
CPUs can be accelerated by zstd column index compression, especially for
semi-structured problems, but likely also for unstructured problems
numbered by br
CPU to GPU? Especially matrices?
> On Jul 11, 2019, at 9:05 AM, Jed Brown via petsc-dev
> wrote:
>
> Zstd is a remarkably good compressor. I've experimented with it for
> compressing column indices for sparse matrices on structured grids and
> (after a simple transform: subtracting the row
Zstd is a remarkably good compressor. I've experimented with it for
compressing column indices for sparse matrices on structured grids and
(after a simple transform: subtracting the row number) gotten
decompression speed in the neighborhood of 10 GB/s (i.e., faster per
core than DRAM). I've been
