SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV algorithms to diverse matrices and architectures requires a framework capable of accurately recognizing sparse patterns and selecting the optimal...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Accuracy Libraries Machine learning Machine learning algorithms Neural networks Pattern recognition Prediction algorithms Predictive models Scientific computing Sparse matrices Sparse Matrix SpMV
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