Performance Optimization of 3D Stencil Computation on ARM Scalable Vector Extension

Fuente: arXiv
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Autore principale: Chen, Hongguang
Natura: Preprint
Pubblicazione: 2025
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author Chen, Hongguang
author_facet Chen, Hongguang
contents Stencil computation is essential in high-performance computing, especially for large-scale tasks like liquid simulation and weather forecasting. Optimizing its performance can reduce both energy consumption and computation time, which is critical in disaster prediction. This paper explores optimization techniques for 7-point 3D stencil computation on ARM's Scalable Vector Extension (SVE), using the Roofline model and tools like Gem5 and cacti. We evaluate software optimizations such as vectorization and tiling, as well as hardware adjustments in ARM SVE vector lengths and cache configurations. The study also examines performance, power consumption, and chip area trade-offs to identify optimal configurations for ARM-based systems.
format Preprint
id arxiv_https___arxiv_org_abs_2503_01348
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Performance Optimization of 3D Stencil Computation on ARM Scalable Vector Extension
Chen, Hongguang
Performance
Stencil computation is essential in high-performance computing, especially for large-scale tasks like liquid simulation and weather forecasting. Optimizing its performance can reduce both energy consumption and computation time, which is critical in disaster prediction. This paper explores optimization techniques for 7-point 3D stencil computation on ARM's Scalable Vector Extension (SVE), using the Roofline model and tools like Gem5 and cacti. We evaluate software optimizations such as vectorization and tiling, as well as hardware adjustments in ARM SVE vector lengths and cache configurations. The study also examines performance, power consumption, and chip area trade-offs to identify optimal configurations for ARM-based systems.
title Performance Optimization of 3D Stencil Computation on ARM Scalable Vector Extension
topic Performance
url https://arxiv.org/abs/2503.01348