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Research Paper
:
Sparse Linear Algebra on AMD and NVIDIA GPUs -- The Race is on
Event Type
Research Paper
Passes
Tags
HPC Accelerators
Math Library Design
Parallel Algorithms
Performance Analysis and Optimization
TimeWednesday, June 24th11:30am - 12:00pm
LocationAnalog 1, 2
DescriptionEfficiently processing sparse matrices is a central and performance-critical part of many scientific simulation codes. Recognizing the adoption of manycore accelerators in HPC, we evaluate in this paper the performance of the currently best sparse matrix-vector product (SpMV) implementations on high-end GPUs from AMD and NVIDIA. Specifically, we optimize SpMV kernels for the CSR,COO, ELL, and HYB format taking the hardware characteristics of the latest GPU technologies into account. We compare for 2,800 test matrices the performance of our kernels against AMD's hipSPARSE library and NVIDIA's cuSPARSE library, and ultimately assesshow the GPU technologies from AMD and NVIDIA compare in terms of SpMV performance.