Towards Transparent Checkpointing with AI-driven Code Generation

2026-06-29

AI Summary

Manually implementing checkpoint/restart mechanisms for MPI-based scientific applications is time-consuming and heavily reliant on expert knowledge. This work proposes an automated framework that leverages the Claude Opus 4.7 large language model in conjunction with OpenCode CLI to iteratively generate code, achieving end-to-end fault tolerance injection without human intervention for the first time. Evaluated on six real-world MPI applications, the approach automatically produces valid checkpointing code within an average of 50 minutes, incurs negligible runtime overhead, and achieves recovery performance comparable to handcrafted implementations. This significantly lowers the technical barrier to resilience engineering in high-performance computing applications.

Abstract

Adding reliable checkpoint/restart support to an MPI scientific application is a time-consuming expert effort that requires deep knowledge of both the application and resilience. We ask whether a frontier large language model can perform this work end-to-end without human intervention. We assemble a benchmark suite of MPI applications spanning diverse domains and computation patterns, and drive an iterative code-generation loop for each application using Anthropic's Claude Opus 4.7 invoked through the OpenCode CLI. Across six scientific applications, the LLM generates working checkpoint/restart code in 50 minutes on average while consuming 3.4 M tokens per application. The generated code adds negligible overhead during normal failure-free execution on five of six applications and recovers from injected process failures with efficiency comparable to human-engineered checkpoint/restart implementations. These results suggest that automated end-to-end LLM-driven resilience engineering is technically viable today for a meaningful fraction of HPC applications.

Problem

• checkpointing
• resilience
• MPI
• scientific applications
• fault tolerance

Innovation

• LLM-driven code generation
• checkpoint/restart
• MPI resilience
• automated fault tolerance
• HPC