Report on Challenges of Practical Reproducibility for Systems and HPC Computer Science
By: Kate Keahey , Marc Richardson , Rafael Tolosana Calasanz and more
Potential Business Impact:
Makes computer science experiments easier to repeat.
This report synthesizes findings from the November 2024 Community Workshop on Practical Reproducibility in HPC, which convened researchers, artifact authors, reviewers, and chairs of reproducibility initiatives to address the critical challenge of making computational experiments reproducible in a cost-effective manner. The workshop deliberately focused on systems and HPC computer science research due to its unique requirements, including specialized hardware access and deep system reconfigurability. Through structured discussions, lightning talks, and panel sessions, participants identified key barriers to practical reproducibility and formulated actionable recommendations for the community. The report presents a dual framework of challenges and recommendations organized by target audience (authors, reviewers, organizations, and community). It characterizes technical obstacles in experiment packaging and review, including completeness of artifact descriptions, acquisition of specialized hardware, and establishing reproducibility conditions. The recommendations range from immediate practical tools (comprehensive checklists for artifact packaging) to ecosystem-level improvements (refining badge systems, creating artifact digital libraries, and developing AI-assisted environment creation). Rather than advocating for reproducibility regardless of cost, the report emphasizes striking an appropriate balance between reproducibility rigor and practical feasibility, positioning reproducibility as an integral component of scientific exploration rather than a burdensome afterthought. Appendices provide detailed, immediately actionable checklists for authors and reviewers to improve reproducibility practices across the HPC community.
Similar Papers
Addressing Reproducibility Challenges in HPC with Continuous Integration
Distributed, Parallel, and Cluster Computing
Makes computer programs easier to check and reuse.
A Framework for Supporting the Reproducibility of Computational Experiments in Multiple Scientific Domains
Software Engineering
Makes science experiments always give the same results.
Let's Talk About It: Making Scientific Computational Reproducibility Easy
Human-Computer Interaction
Lets scientists easily redo experiments on any computer.