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.

翻译：AMD MI300A APU 集成了 CDNA3 GPU、高带宽内存及先进的加速器特性：FP8 矩阵核心、异步计算引擎（ACE）以及 2:4 结构化稀疏性。现代高性能计算（HPC）与 HPC-AI 工作负载日益依赖这些能力，然而其执行特性与系统级影响仍未得到充分理解。本文通过针对性微基准测试，对 MI300A 上的 FP8 矩阵执行、ACE 并发性及结构化稀疏性进行了以执行中心化的表征。我们量化了占用率阈值、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——类 Transformer、并发及混合精度内核——以展示这些效应如何转化为应用级性能与可预测性。我们的结果为 MI300A 类统一节点上基于占用率的调度、并发决策及稀疏性启用提供了实用指导。