标题： 利用硬件感知计算的混合精度矩阵乘法：一种基于块的方法 显示英文标题

标题： Leveraging Hardware-Aware Computation in Mixed-Precision Matrix Multiply: A Tile-Centric Approach

摘要： 通用矩阵乘法（GEMM）是支撑高性能计算（HPC）和人工智能（AI）广泛应用的关键操作。 针对低精度算术优化的硬件的出现，需要重新评估数值算法，以利用混合精度计算，实现性能和能效的提升。 本研究引入了一个自适应混合精度GEMM框架，可在细粒度的块/ tile级别支持不同的精度格式。 我们利用PaRSEC运行时系统在各种架构上平衡工作负载。 该性能在基于ARM CPU的Fugaku超级计算机、基于Nvidia GPU的A100 DGX和基于AMD GPU的Frontier超级计算机上表现良好。 本研究旨在通过弥合算法进步与硬件创新之间的差距，提高计算效率和准确性，推动各种应用的变革性进展。 显示英文摘要

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 introduces an adaptive mixed-precision GEMM framework that supports different precision formats at fine-grained tile/block levels. We utilize the PaRSEC runtime system to balance workloads across various architectures. The performance scales well on ARM CPU-based Fugaku supercomputer, Nvidia GPU-based A100 DGX, and AMD GPU-based Frontier supercomputer. This research aims to enhance computational efficiency and accuracy by bridging algorithmic advancements and hardware innovations, driving transformative progress in various applications.