SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation

초록

Sparse matrix-sparse matrix multiplication (SpMSpM) is crucial in many fields such asscientific computing, sparse linear algebra, and machine learning due to its computational complexity inthe large and extremely sparse datasets. Various applications dealing with the sparse matrix show a varietyof sparse matrix patterns, so the inner product, outer product, and Gustavson (row-wise) methods havebeen selectively used for the acceleration of the sparse matrix computation. Previous works determine afixed dataflow before the computation. However, such an approach cannot optimize all the input matricetypes having various data patterns. To address these limitations, we propose a SpecBoost, a method thatdynamically selects an optimal tile-level SpMSpM dataflow by analyzing the sparsity pattern within eachmatrix tile and speculating the best tiled dataflow scheme before the computational stage. We compared ourmethod with the widely known previous methods (CSSpa, ExTensor, MatRaptor), and experimental resultsshow that on average our method reduced memory accesses by a factor of (4.01x, 2.86x, 2.22x) and booststhe performance of prior works over the baseline by (4.62x, 2.40x, 1.59x).

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