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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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
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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Abstract 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 implementation. In this work, we introduce Sparsity-aware SpMV (SSpMV), a framework that integrates expert-designed features with multimodal representations to adaptively predict the best-performing algorithm and parameters. For this purpose, we design a multimodal neural network called MM-Adapter, to capture diverse modalities to represent the computational features of SpMV. Experimental results demonstrate that MMAdapter achieves the highest accuracy of 81.05 \%, outperforming existing SpMV prediction models. Furthermore, SSpMV consistently delivers substantial performance improvements over state-of-the-art sparse libraries across various multi-core platforms.
AbstractList 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 implementation. In this work, we introduce Sparsity-aware SpMV (SSpMV), a framework that integrates expert-designed features with multimodal representations to adaptively predict the best-performing algorithm and parameters. For this purpose, we design a multimodal neural network called MM-Adapter, to capture diverse modalities to represent the computational features of SpMV. Experimental results demonstrate that MMAdapter achieves the highest accuracy of 81.05 \%, outperforming existing SpMV prediction models. Furthermore, SSpMV consistently delivers substantial performance improvements over state-of-the-art sparse libraries across various multi-core platforms.
Author Zhou, Joey Tianyi
Ding, Yan
Li, Kenli
Lin, Shengle
Yang, Wangdong
Liu, Chubo
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  givenname: Joey Tianyi
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  givenname: Wangdong
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  fullname: Yang, Wangdong
  email: yangwangdong@hnu.edu.cn
  organization: Hunan University,College of Computer Science and Electronic Engineering,China
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Snippet Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV...
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SubjectTerms Accuracy
Libraries
Machine learning
Machine learning algorithms
Neural networks
Pattern recognition
Prediction algorithms
Predictive models
Scientific computing
Sparse matrices
Sparse Matrix
SpMV
Title SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning
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