Scholarlink

<p>General Matrix Multiplication (GEMM) is a critical operation underpinning a wide range of applications in high-performance computing (HPC) and artificial intelligence (AI). The emergence of hardware optimized for low-precision arithmetic necessitates a reevaluation of numerical algorithms to leverage mixed-precision computations, achieving improved performance and energy efficiency. This research presents an adaptive mixed-precision GEMM framework that enables support for various precision formats at fine-grained tile and block levels, offering a reliable foundation for trustworthy mixed-precision computations. Furthermore, we leverage the PaRSEC runtime system to effectively balance workloads across diverse architectures. The performance exhibits strong scalability across both homogeneous platforms (Intel CPU-based systems and the ARM CPU-based <Emphasis FontCategory="NonProportional">Fugaku</Emphasis> supercomputer) and heterogeneous systems (Nvidia V100, A100, and H100 GPU-based platforms, as well as the AMD GPU-based <Emphasis FontCategory="NonProportional">Frontier</Emphasis> supercomputer). This work aims to improve computational efficiency and accuracy by bridging algorithmic innovations with hardware capabilities, fostering transformative advancements across a wide range of applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

High-Performance Mixed-Precision Matrix Multiplication via Tile-Centric Design on Modern Architectures

  • 期刊论文

  • 发表日期：

    2025年12月20日

查看全文
  • Qiao Zhang,
  • Rabab Alomairy,
  • Dali Wang,
  • Zhuowei Gu,
  • Qinglei Cao

摘要

General Matrix Multiplication (GEMM) is a critical operation underpinning a wide range of applications in high-performance computing (HPC) and artificial intelligence (AI). The emergence of hardware optimized for low-precision arithmetic necessitates a reevaluation of numerical algorithms to leverage mixed-precision computations, achieving improved performance and energy efficiency. This research presents an adaptive mixed-precision GEMM framework that enables support for various precision formats at fine-grained tile and block levels, offering a reliable foundation for trustworthy mixed-precision computations. Furthermore, we leverage the PaRSEC runtime system to effectively balance workloads across diverse architectures. The performance exhibits strong scalability across both homogeneous platforms (Intel CPU-based systems and the ARM CPU-based Fugaku supercomputer) and heterogeneous systems (Nvidia V100, A100, and H100 GPU-based platforms, as well as the AMD GPU-based Frontier supercomputer). This work aims to improve computational efficiency and accuracy by bridging algorithmic innovations with hardware capabilities, fostering transformative advancements across a wide range of applications.