Caliper: Performance Introspection for HPC Software Stacks

TLDR

Performance engineering tasks—from long‑term monitoring to post‑mortem analysis and online tuning—require efficient runtime introspection and data collection, yet existing tools lack an interoperable, cross‑stack, general‑purpose approach. The study aims to integrate performance introspection hooks across runtime systems, libraries, and application codes to better understand interactions in modular HPC software. Caliper implements a general abstraction layer that offers performance data collection as a service, enabling components to act as independent data producers, consumers, and measurement controllers and share data across stack boundaries. Two production‑scenario case studies demonstrate Caliper's performance analysis capabilities.

Abstract

Many performance engineering tasks, from long-term performance monitoring to post-mortem analysis and online tuning, require efficient runtime methods for introspection and performance data collection. To understand interactions between components in increasingly modular HPC software, performance introspection hooks must be integrated into runtime systems, libraries, and application codes across the software stack. This requires an interoperable, cross-stack, general-purpose approach to performance data collection, which neither application-specific performance measurement nor traditional profile or trace analysis tools provide. With Caliper, we have developed a general abstraction layer to provide performance data collection as a service to applications, runtime systems, libraries, and tools. Individual software components connect to Caliper in independent data producer, data consumer, and measurement control roles, which allows them to share performance data across software stack boundaries. We demonstrate Caliper's performance analysis capbilities with two case studies of production scenarios.