Application Failures and Machine Computational Efficiency

2026-08-05

AI Summary

This work addresses the lack of resource-centric computational efficiency metrics—specifically in terms of node-hours—for existing supercomputers and large-scale AI training platforms operating under high failure rates. It proposes the first efficiency evaluation framework grounded in resource consumption rather than execution time, unifying failure rate, mean time between failures, and checkpoint/restart overhead into a cohesive resource-based model. The framework extends Daly’s (2006) model to accommodate heterogeneous scientific workloads. Validated on one year of production data from the Frontier supercomputer, the approach leverages runtime log analysis, joint modeling of failures and checkpointing, and optimization algorithms to accurately quantify the expected fraction of resources usable for scientific computation and to determine optimal checkpoint intervals that minimize resource loss.

Abstract

We present a framework for evaluating uptime efficiency of Exascale-class scientific computers when application failure rates are appreciable. This is the situation that confronts current leadership-class scientific computing platforms and large AI training installations. What distinguishes scientific computing platforms is the heterogeneity of their applications. We argue that this diversity requires that failure rates and mean intervals between failures should be specified in terms of \emph{usage} (e.g. node-hours) rather than time, as is currently customary. We consider the usage loss terms due to failures, to checkpointing, and to restart costs, and update the framework of Daly (2006) allowing users to specify optimal checkpointing usage intervals that minimize such losses. We derive the machine computational efficiency, which specifies the expected fractional resource allocation that is available for scientific computation. We illustrate the methodology using one year of production runtime data from the \emph{Frontier} supercomputer at Oak Ridge National Laboratory.

Problem

• application failures
• computational efficiency
• Exascale computing
• uptime efficiency
• heterogeneous applications

Innovation

• computational efficiency
• checkpointing optimization
• usage-based failure modeling
• exascale computing
• heterogeneous applications