| Title | Improving Reproducibility of Distributed Computational Experiments |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Pham, Quan, Malik, Tanu, That, Dai Hai Ton, Youngdahl, Andrew |
| Conference Name | Proceedings of the First International Workshop on Practical Reproducible Evaluation of Computer Systems |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5861-3 |
| Keywords | composability, Human Behavior, Metrics, Network provenance, Provenance, pubcrawl, record and replay, reproducibility of distributed objects, Resiliency, sciunit |
| Abstract | Conference and journal publications increasingly require experiments associated with a submitted article to be repeatable. Authors comply to this requirement by sharing all associated digital artifacts, i.e., code, data, and environment configuration scripts. To ease aggregation of the digital artifacts, several tools have recently emerged that automate the aggregation of digital artifacts by auditing an experiment execution and building a portable container of code, data, and environment. However, current tools only package non-distributed computational experiments. Distributed computational experiments must either be packaged manually or supplemented with sufficient documentation. In this paper, we outline the reproducibility requirements of distributed experiments using a distributed computational science experiment involving use of message-passing interface (MPI), and propose a general method for auditing and repeating distributed experiments. Using Sciunit we show how this method can be implemented. We validate our method with initial experiments showing application re-execution runtime can be improved by 63% with a trade-off of longer run-time on initial audit execution. |
| URL | http://doi.acm.org/10.1145/3214239.3214241 |
| DOI | 10.1145/3214239.3214241 |
| Citation Key | pham_improving_2018 |
Improving Reproducibility of Distributed Computational Experiments