Weird. A library that wraps SYCL within MPI, yet requires all processes to hold a copy of all the memory ?
One of the main reasons to use MPI is to solve problems that do not fit within the memory available in a single cluster node.
There is a presentation [0] from 2020-02-20 that's not very impressive. Particularly, they compare against MPI+OpenCL, but do not show a comparison against MPI-CUDA.
Doing a distributed MatMul using MPI-CUDA is trivial. I wonder how the cyclomatic complexity and performance compares for that case. Insted, they only compare doing one MatMul per process using MPI-OpenCL... that's... a two liner with CUDA (just call MpiInit followed by a cuBLAS call).
One of the main reasons to use MPI is to solve problems that do not fit within the memory available in a single cluster node.
There is a presentation [0] from 2020-02-20 that's not very impressive. Particularly, they compare against MPI+OpenCL, but do not show a comparison against MPI-CUDA.
Doing a distributed MatMul using MPI-CUDA is trivial. I wonder how the cyclomatic complexity and performance compares for that case. Insted, they only compare doing one MatMul per process using MPI-OpenCL... that's... a two liner with CUDA (just call MpiInit followed by a cuBLAS call).
[0] https://www.uibk.ac.at/fz-hpc/events/resources/2020_02_20_ah...