AIPerf: Automated machine learning as an AI-HPC benchmark

TL;DR

AIPerf introduces an automated, scalable AI-HPC benchmark suite using AutoML, capable of evaluating diverse systems with a unified metric, addressing limitations of existing benchmarks.

Contribution

It presents a novel AutoML-based benchmark that is scalable, flexible, and provides a unified performance metric for AI-HPC systems.

Findings

Achieved near-linear weak scalability up to 512 nodes.

Measured 56.1 Tera-OPS on 4 nodes and 194.53 Peta-OPS on 512 nodes.

Demonstrated the benchmark's stability and adaptability across diverse systems.

Abstract

The plethora of complex artificial intelligence (AI) algorithms and available high performance computing (HPC) power stimulates the expeditious development of AI components with heterogeneous designs. Consequently, the need for cross-stack performance benchmarking of AI-HPC systems emerges rapidly. The de facto HPC benchmark LINPACK can not reflect AI computing power and I/O performance without representative workload. The current popular AI benchmarks like MLPerf have fixed problem size therefore limited scalability. To address these issues, we propose an end-to-end benchmark suite utilizing automated machine learning (AutoML), which not only represents real AI scenarios, but also is auto-adaptively scalable to various scales of machines. We implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable configurations. We utilize operations per second (OPS), which is measured in an analytical and systematic approach, as the major metric to quantify the AI performance. We perform evaluations on various systems to ensure the benchmark's stability and scalability, from 4 nodes with 32 NVIDIA Tesla T4 (56.1 Tera-OPS measured), up to 512 nodes with 4096 Huawei Ascend 910 (194.53 Peta-OPS measured), and the results show near-linear weak scalability. With flexible workload and single metric, our benchmark can scale and rank AI-HPC easily.