FAIR Sharing of Data in Autotuning Research (Vision Paper)
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | COMPANION OF THE 15TH ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING, ICPE COMPANION 2024 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1145/3629527.3651429 |
| Keywords | autotuning; benchmarks; performance; measurements; open data; data sharing |
| Description | Autotuning is an automated process that selects the best computer program implementation from a set of candidates to improve performance, such as execution time, when run under new circumstances, such as new hardware. The process of autotuning generates a large amount of performance data with multiple potential use cases, including reproducing results, comparing included methods, and understanding the impact of individual tuning parameters. We propose the adoption of FAIR Principles, which stands for Findable, Accessible, Interoperable, and Reusable, to organize the guidelines for data sharing in autotuning research. The guidelines aim to lessen the burden of sharing data and provide a comprehensive checklist of recommendations for shared data. We illustrate three examples that could greatly benefit from shared autotuning data to advance the research without time- and resource-demanding data collection. |
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