Modular Acceleration: Tricky Cases of Functional High-performance Computing

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationFHPC 2018 - Proceedings of the 7th ACM SIGPLAN International Workshop on Functional High-Performance Computing, co-located with ICFP 2018
EditorsMike Rainey, Kei Davis
Number of pages12
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Publication date2018
Pages10-21
ISBN (Print)978-1-4503-5813-2
ISBN (Electronic)9781450358132
DOIs
Publication statusPublished - 2018
Event7th ACM SIGPLAN International Workshop on Functional High-Performance Computing - St. Louis, United States
Duration: 29 Sep 201829 Sep 2018

Workshop

Workshop7th ACM SIGPLAN International Workshop on Functional High-Performance Computing
LandUnited States
BySt. Louis
Periode29/09/201829/09/2018

    Research areas

  • Compilers, GPU, Parallelism

ID: 204479272