Year: 2022
Type: preprint
Abstract: We compare automatically and manually parallelized NAS Benchmarks in order to identify code sections that differ. We discuss opportunities for advancing automatic parallelizers. We find ten patterns that pose challenges for current parallelization technology. We also measure the potential impact of advanced techniques that could perform the needed transformations automatically. While some of our findings are not surprising and difficult to attain -- compilers need to get better at identifying parallelism in outermost loops and in loops containing function calls -- other opportunities are within reach and can make a difference. They include combining loops into parallel regions, avoiding load imbalance, and improving reduction parallelization. Advancing compilers through the study of hand-optimized code is a necessary path to move the forefront of compiler research. Very few recent papers have pursued this goal, however. The present work tries to fill this void.
Source: arXiv (Cornell University)
Authors: Barakhshan, Parinaz, Eigenmann, Rudolf
Institution: University of Delaware
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: EAGER: The Xpert Network: Synergizing National Expert-Assistance and Tool-Support Teams for Computational and Data-Intensive Science, Elements: Scalable and Automated Atomic Portal - Bridging the Gap Between Research Codes and User Community, Elements: Community portal for high-precision atomic physics data and computation
OpenAlex ID: W4310628700