Software engineering to sustain a high-performance computing scientific application: QMCPACK

TL;DR

This paper discusses the software engineering strategies implemented in QMCPACK to enhance its sustainability, maintainability, and performance on high-performance computing systems, emphasizing continuous integration, containerization, and code refactoring.

Contribution

It introduces specific software engineering practices applied to QMCPACK, demonstrating their impact on code sustainability and scientific productivity in HPC environments.

Findings

Improved CI coverage for CPU and GPU architectures.

Reduced memory leaks through sanitizers.

Enhanced reproducibility with Docker containers.

Abstract

We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions runners, and NVIDIA and AMD GPUs in pre-exascale systems, using self-hosted hardware; (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, sustainable maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the sustainability of community HPC codes and scientific discovery at scale.