Characterizing Scalability of Sparse Matrix-Vector Multiplications on Phytium FT-2000+ Many-cores

TL;DR

This paper investigates the scalability of sparse matrix-vector multiplication on Phytium FT-2000+ many-core architecture, providing empirical data and a regression tree-based model to guide optimization efforts for HPC applications.

Contribution

It offers the first large-scale empirical analysis of SpMV scalability on ARM-based many-core hardware and introduces a regression tree model for predicting performance.

Findings

Achieving linear speedup on Phytium FT-2000+ is challenging.

The regression tree model effectively predicts SpMV scalability.

Extensive parallelism exists but is hard to fully utilize.

Abstract

Understanding the scalability of parallel programs is crucial for software optimization and hardware architecture design. As HPC hardware is moving towards many-core design, it becomes increasingly difficult for a parallel program to make effective use of all available processor cores. This makes scalability analysis increasingly important. This paper presents a quantitative study for characterizing the scalability of sparse matrix-vector multiplications (SpMV) on Phytium FT-2000+, an ARM-based many-core architecture for HPC computing. We choose to study SpMV as it is a common operation in scientific and HPC applications. Due to the newness of ARM-based many-core architectures, there is little work on understanding the SpMV scalability on such hardware design. To close the gap, we carry out a large-scale empirical evaluation involved over 1,000 representative SpMV datasets. We show that, while many computation-intensive SpMV applications contain extensive parallelism, achieving a linear speedup is non-trivial on Phytium FT-2000+. To better understand what software and hardware parameters are most important for determining the scalability of a given SpMV kernel, we develop a performance analytical model based on the regression tree. We show that our model is highly effective in characterizing SpMV scalability, offering useful insights to help application developers for better optimizing SpMV on an emerging HPC architecture.