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DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
SessionLinear Algebra II
DescriptionSparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. Much SpMV work was devoted to resolving problems such as random access to vector x and unbalanced load. However, we have experimentally found that the compute of inner products still occupies much overhead in SpMV operation, which has been largely ignored in existing work.
In this paper, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV. The experimental results on the latest NVIDIA Ampere and Hopper GPUs show that our DASP brought significant speedups over state-of-the-art SpMV work.
In this paper, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV. The experimental results on the latest NVIDIA Ampere and Hopper GPUs show that our DASP brought significant speedups over state-of-the-art SpMV work.
Event Type
Paper
TimeThursday, 16 November 202310:30am - 11am MST
Location401-402
Algorithms
Linear Algebra
Post-Moore Computing
TP
