Abstract
This case study examines the data-parallel functional implementation of three algorithms: generation of quasi-random Sobol numbers, breadth-first search, and calibration of Heston market parameters via a least-squares procedure. We show that while all these problems permit elegant functional implementations, good performance depends on subtle issues that must be confronted in both the implementations of the algorithms themselves, as well as the compiler that is responsible for ultimately generating high-performance code. In particular, we demonstrate a modular technique for generating quasi-random Sobol numbers in an efficient manner, study the efficient implementation of an irregular graph algorithm without sacrificing parallelism, and argue for the utility of nested regular data parallelism in the context of nonlinear parameter calibration.
| Original language | English |
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| Title of host publication | FHPC 2018 - Proceedings of the 7th ACM SIGPLAN International Workshop on Functional High-Performance Computing, co-located with ICFP 2018 |
| Editors | Mike Rainey, Kei Davis |
| Number of pages | 12 |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery |
| Publication date | 2018 |
| Pages | 10-21 |
| ISBN (Print) | 978-1-4503-5813-2 |
| ISBN (Electronic) | 9781450358132 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 7th ACM SIGPLAN International Workshop on Functional High-Performance Computing - St. Louis, United States Duration: 29 Sept 2018 → 29 Sept 2018 |
Workshop
| Workshop | 7th ACM SIGPLAN International Workshop on Functional High-Performance Computing |
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| Country/Territory | United States |
| City | St. Louis |
| Period | 29/09/2018 → 29/09/2018 |
Keywords
- Compilers
- GPU
- Parallelism