Authors: Daniel Nichols and Dilan Gunawardana (University of Maryland), Aniruddha Marathe and Todd Gamblin (Lawrence Livermore National Laboratory), and Abhinav Bhatele (University of Maryland)
Abstract: Scientific software in high performance computing is becoming increasingly complex both in terms of its size and the number of external dependencies. Correctness and performance issues can become more challenging in actively developed software with increasing complexity. This leads to software developers having to spend larger portions of their time on debugging, optimizing, and maintaining code. Making software optimization and maintenance easier for developers is paramount to accelerating the rate of scientific progress. Fortunately, there is a wealth of data on scientific coding practices available implicitly via version control histories. These contain the state of a code at each stage throughout its development via commit snapshots. Commit snapshots provide dynamic insight into the software development process that static analyses of release tarballs do not. We propose a new machine learning based approach for studying the performance of source code across code modifications.
Best Poster Finalist (BP): no
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