Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems

TL;DR

This paper investigates the performance variability in large-scale GPU-accelerated systems, revealing significant differences even among identical GPUs, which impacts efficiency and future system design.

Contribution

It provides the first comprehensive characterization of GPU performance variability across multiple large-scale HPC systems, highlighting its prevalence and implications.

Findings

Average performance variation of 8% across GPUs

Outliers up to 1.5 times slower than median

Variability consistent across different applications and cooling methods

Abstract

Scientists are increasingly exploring and utilizing the massive parallelism of general-purpose accelerators such as GPUs for scientific breakthroughs. As a result, datacenters, hyperscalers, national computing centers, and supercomputers have procured hardware to support this evolving application paradigm. These systems contain hundreds to tens of thousands of accelerators, enabling peta- and exa-scale levels of compute for scientific workloads. Recent work demonstrated that power management (PM) can impact application performance in CPU-based HPC systems, even when machines have the same architecture and SKU (stock keeping unit). This variation occurs due to manufacturing variability and the chip's PM. However, while modern HPC systems widely employ accelerators such as GPUs, it is unclear how much this variability affects applications. Accordingly, we seek to characterize the extent of variation due to GPU PM in modern HPC and supercomputing systems. We study a variety of applications that stress different GPU components on five large-scale computing centers with modern GPUs: Oak Ridge's Summit, Sandia's Vortex, TACC's Frontera and Longhorn, and Livermore's Corona. These clusters use a variety of cooling methods and GPU vendors. In total, we collect over 18,800 hours of data across more than 90% of the GPUs in these clusters. Regardless of the application, cluster, GPU vendor, and cooling method, our results show significant variation: 8% (max 22%) average performance variation even though the GPU architecture and vendor SKU are identical within each cluster, with outliers up to 1.5X slower than the median GPU. These results highlight the difficulty in efficiently using existing GPU clusters for modern HPC and scientific workloads, and the need to embrace variability in future accelerator-based systems.