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Algorithm/Hardware Co-Optimization for Sparsity-Aware SpMM Acceleration of GNNs by Yingxue Gao; Lei Gong; Chao Wang; Teng Wang; Xi Li; Xuehai Zhou is a Engineering article available to read on EtoBox.

What is Algorithm/Hardware Co-Optimization for Sparsity-Aware SpMM Acceleration of GNNs about?

In recent years, graph neural networks (GNNs) have achieved impressive performance in various application fields by extracting information from graph-structured data. It contains extensive feature aggregation operations and has become a performance bottleneck, which can be abstracted as a specialized Sparse-Dense Matrix Multiplication (SpMM) operation. Previous works have leveraged the inner product or outer product to accelerate the feature aggregation process. However, inefficient execution leads to extremely unbalanced workloads and extensive intermediate data, hampering the performance of previous processors. So in this paper, we demonstrate an algorithm/hardware co-optimization chance to enhance SpMM acceleration for GNNs. First, the algorithm part develops a dataflow-efficient SpMM algorithm that integrates three optimization methods to mitigate computation and memory access inefficiencies. Specifically, 1) the proposed equal-value partition method achieves fine-grained data partition and enables load balancing during data movement. 2) After observing the vertex aggregation phenomenon, a vertexclustering optimization method is presented to enable significant data locality. 3)

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Author

Yingxue Gao; Lei Gong; Chao Wang; Teng Wang; Xi Li; Xuehai Zhou

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)