Jiafan Jiang
ABSTRACT
Sparse Matrix-Vector Multiplication (SpMV) has been widely used in the field of scientific computing. Optimization of SpMV’s computational performance can bring significant benefits to its usage. In recent years, with the development of GPU hardware technology, SpMV can provide tens of times better performance than CPUs for simple computational tasks. Optimization of SpMV on the GPU platform can bring great performance improvements. Load balancing is crucial for achieving performance improvements when using GPU platforms with thousands of computing cores. This study proposes a two-level average block partitioning strategy based on the CSR format to achieve balanced block-level and thread-level task partitioning. The study also designed a parallel merge scheme for data merging among threads, warps, and blocks, further improving the parallelism of SpMV computation.
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Index Terms
- GTLB:A Load-Balanced SpMV Computation Method on GPU
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