Execution-Centric Characterization of FP8 Matrix Cores, Asynchronous Execution, and Structured Sparsity on AMD MI300A

2026-02-10

AI Summary

This study addresses the limited understanding of the synergistic interplay among FP8 matrix cores, asynchronous execution, and 2:4 structured sparsity on AMD MI300A systems, a gap that hinders performance optimization for HPC and HPC-AI workloads. Through custom microbenchmarks, Transformer-style kernels, and mixed-precision concurrency tests—augmented by hardware performance counters and runtime analysis—the work provides the first systematic characterization of their interaction dynamics. It quantifies concurrency throughput and fairness boundaries, revealing the context-dependent efficacy of structured sparsity. The research identifies critical occupancy thresholds and conditions under which sparsity yields tangible benefits, and proposes practical scheduling and sparsity activation strategies for real-world deployment, offering actionable guidance for performance tuning on MI300A-class architectures.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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.

Problem

• FP8 matrix cores
• asynchronous execution
• structured sparsity
• MI300A
• execution characterization

Innovation

• FP8 matrix cores
• asynchronous execution
• structured sparsity
• execution-centric characterization
• concurrent occupancy