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ICFP 2018
Sun 23 - Sat 29 September 2018 St. Louis, Missouri, United States
Sat 29 Sep 2018 10:20 - 11:20 at New York Central - Morning Session 2 Chair(s): Kei Davis

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 the utility of strength-reducing ``chunking'' combinators that permit efficient sequentialisation of excess parallelism, study the efficient implementation of an irregular algorithm without sacrificing parallelism, and argue for the utility of nested regular data parallelism.

Sat 29 Sep

Displayed time zone: Guadalajara, Mexico City, Monterrey change

10:20 - 12:15
Morning Session 2FHPC at New York Central
Chair(s): Kei Davis Los Alamos National Laboratory
Modular Acceleration: Tricky Cases of Functional High-Performance Computing
A: Troels Henriksen University of Copenhagen, Denmark, A: Martin Elsman University of Copenhagen, Denmark, A: Cosmin Oancea University of Copenhagen, Denmark
Preventing Data Races with Refinement Types