面向执行的FP8矩阵核心、异步执行与结构稀疏性在AMD MI300A上的特性分析
Execution-Centric Characterization of FP8 Matrix Cores, Asynchronous Execution, and Structured Sparsity on AMD MI300A
AI总结：
本文通过微观基准测试分析了AMD MI300A上的FP8矩阵执行、ACE并发性和结构稀疏性，揭示了其执行特性及对HPC和HPC-AI工作负载性能的影响。
摘要：
AMD MI300A APU集成了CDNA3 GPU、高带宽内存和先进加速器功能：FP8矩阵核心、异步计算引擎（ACE）和2:4结构稀疏性。这些能力越来越被现代高性能计算（HPC）和HPC-AI工作负载所依赖，但其执行特性和系统级影响仍不够理解。在本文中，我们通过针对性的微观基准测试，对MI300A上的FP8矩阵执行、ACE并发性和结构稀疏性进行了面向执行的特性分析。我们量化了占用阈值、公平性、并发执行下的吞吐量权衡以及上下文相关的稀疏性收益。我们评估了代表性案例研究——Transformer风格、并发和混合精度内核——以展示这些效应如何转化为应用级性能和可预测性。我们的结果为MI300A类统一节点上的占用感知调度、并发决策和稀疏性启用提供了实际指导。
英文摘要：
The AMD MI300A APU integrates CDNA3 GPUs with high-bandwidth memory and advanced accelerator features: FP8 matrix cores, asynchronous compute engines (ACE), and 2:4 structured sparsity. These capabilities are increasingly relied upon by modern HPC and HPC-AI workloads, yet their execution characteristics and system-level implications remain insufficiently understood. In this paper, we present an execution-centric characterization of FP8 matrix execution, ACE concurrency, and structured sparsity on MI300A using targeted microbenchmarks. We quantify occupancy thresholds, fairness, throughput trade-offs under concurrent execution, and context-dependent sparsity benefits. We evaluate representative case studies - transformer-style, concurrent, and mixed-precision kernels - to show how these effects translate into application-level performance and predictability. Our results provide practical guidance for occupancy-aware scheduling, concurrency decisions, and sparsity enablement on MI300A-class unified nodes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
