A Systematic Literature Survey of Sparse Matrix-Vector Multiplication
Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang

TL;DR
This paper provides a comprehensive systematic survey of recent advancements in sparse matrix-vector multiplication (SpMV), analyzing various optimization techniques across modern hardware architectures and identifying future research challenges.
Contribution
It offers a detailed classification and comparison of SpMV optimization methods, filling a gap in the literature with an extensive experimental evaluation and analysis.
Findings
Performance varies significantly across different optimization techniques.
Auto-tuning and machine learning approaches show promising results.
Several hardware-specific challenges remain unresolved.
Abstract
Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread applications in iterative algorithms. Over the past decades, research on SpMV optimization has made remarkable strides, giving rise to various optimization contributions. However, the comprehensive and systematic literature survey that introduces, analyzes, discusses, and summarizes the advancements of SpMV in recent years is currently lacking. Aiming to fill this gap, this paper compares existing techniques and analyzes their strengths and weaknesses. We begin by highlighting two representative applications of SpMV, then conduct an in-depth overview of the important techniques that optimize SpMV on modern architectures, which we specifically classify as classic, auto-tuning, machine learning, and mixed-precision-based optimization. We also elaborate on the hardware-based architectures, including…
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Taxonomy
TopicsTensor decomposition and applications
