SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation
Sparse matrix-sparse matrix multiplication (SpMSpM) is crucial in many fields such as scientific computing, sparse linear algebra, and machine learning due to its computational complexity in the large and extremely sparse datasets. Various applications dealing with the sparse matrix show a variety o...
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IEEE
2025-01-01
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| author | Gwanghwi Seo Sungju Ryu |
| author_facet | Gwanghwi Seo Sungju Ryu |
| author_sort | Gwanghwi Seo |
| collection | DOAJ |
| description | Sparse matrix-sparse matrix multiplication (SpMSpM) is crucial in many fields such as scientific computing, sparse linear algebra, and machine learning due to its computational complexity in the large and extremely sparse datasets. Various applications dealing with the sparse matrix show a variety of sparse matrix patterns, so the inner product, outer product, and Gustavson (row-wise) methods have been selectively used for the acceleration of the sparse matrix computation. Previous works determine a fixed dataflow before the computation. However, such an approach cannot optimize all the input matrice types having various data patterns. To address these limitations, we propose a SpecBoost, a method that dynamically selects an optimal tile-level SpMSpM dataflow by analyzing the sparsity pattern within each matrix tile and speculating the best tiled dataflow scheme before the computational stage. We compared our method with the widely known previous methods (CSSpa, ExTensor, MatRaptor), and experimental results show that on average our method reduced memory accesses by a factor of (<inline-formula> <tex-math notation="LaTeX">$4.01\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.86\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.22\times $ </tex-math></inline-formula>) and boosts the performance of prior works over the baseline by (<inline-formula> <tex-math notation="LaTeX">$4.62\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.40\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula>). |
| format | Article |
| id | doaj-art-44ff33efd7fa46ffb3b0696a5b49e4d8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-44ff33efd7fa46ffb3b0696a5b49e4d82025-08-20T03:02:55ZengIEEEIEEE Access2169-35362025-01-0113455684557610.1109/ACCESS.2025.355041410921684SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow SpeculationGwanghwi Seo0https://orcid.org/0009-0001-9255-3184Sungju Ryu1https://orcid.org/0000-0002-0254-391XDepartment of Electronic Engineering, Sogang University, Seoul, Republic of KoreaDepartment of System Semiconductor Engineering, Sogang University, Seoul, Republic of KoreaSparse matrix-sparse matrix multiplication (SpMSpM) is crucial in many fields such as scientific computing, sparse linear algebra, and machine learning due to its computational complexity in the large and extremely sparse datasets. Various applications dealing with the sparse matrix show a variety of sparse matrix patterns, so the inner product, outer product, and Gustavson (row-wise) methods have been selectively used for the acceleration of the sparse matrix computation. Previous works determine a fixed dataflow before the computation. However, such an approach cannot optimize all the input matrice types having various data patterns. To address these limitations, we propose a SpecBoost, a method that dynamically selects an optimal tile-level SpMSpM dataflow by analyzing the sparsity pattern within each matrix tile and speculating the best tiled dataflow scheme before the computational stage. We compared our method with the widely known previous methods (CSSpa, ExTensor, MatRaptor), and experimental results show that on average our method reduced memory accesses by a factor of (<inline-formula> <tex-math notation="LaTeX">$4.01\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.86\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.22\times $ </tex-math></inline-formula>) and boosts the performance of prior works over the baseline by (<inline-formula> <tex-math notation="LaTeX">$4.62\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.40\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula>).https://ieeexplore.ieee.org/document/10921684/Matrix tilingsparse matrix multiplicationtile-level dataflow speculatormatrix sampling with threshold |
| spellingShingle | Gwanghwi Seo Sungju Ryu SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation IEEE Access Matrix tiling sparse matrix multiplication tile-level dataflow speculator matrix sampling with threshold |
| title | SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation |
| title_full | SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation |
| title_fullStr | SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation |
| title_full_unstemmed | SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation |
| title_short | SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation |
| title_sort | specboost accelerating tiled sparse matrix multiplication via dataflow speculation |
| topic | Matrix tiling sparse matrix multiplication tile-level dataflow speculator matrix sampling with threshold |
| url | https://ieeexplore.ieee.org/document/10921684/ |
| work_keys_str_mv | AT gwanghwiseo specboostacceleratingtiledsparsematrixmultiplicationviadataflowspeculation AT sungjuryu specboostacceleratingtiledsparsematrixmultiplicationviadataflowspeculation |