ICFP Workshops 2018
23nd ACM SIGPLAN International Conference on Functional Programming (ICFP 2018)
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7th ACM SIGPLAN International Workshop on Functional High-Performance Computing (FHPC 2018), September 29, 2018, St. Louis, MO, USA

FHPC 2018 – Proceedings

Contents - Abstracts - Authors

7th ACM SIGPLAN International Workshop on Functional High-Performance Computing (FHPC 2018)

Title Page

Message from the Chairs
Welcome to the 7th ACM SIGPLAN Workshop on Functional High Performance Computing (FHPC 2018), September 29, St. Louis, Missouri, USA, co-located with the 23rd ACM SIGPLAN International Conference on Functional Programming and Strange Loop. We seek to bring together researchers and practitioners exploring uses of functional (or more generally, declarative or high-level) programming systems or concepts in application domains where high performance is essential. The aim of the meeting is to enable sharing of results, experiences, and novel ideas about how high-level, declarative specifications of computationally challenging problems can serve as maintainable and portable code that approaches (or even exceeds) the performance of machine-oriented imperative implementations.

HELIX: A Case Study of a Formal Verification of High Performance Program Generation
Vadim Zaliva and Franz Franchetti
(Carnegie Mellon University, USA)
In this paper, we present HELIX, a formally verified operator language and rewriting engine for generation of high-performance implementation for a variety of linear algebra algorithms. Based on the existing SPIRAL system, HELIX adds the rigor of formal verification of its correctness using Coq proof assistant. It formally defines two domain-specific languages: HCOL, which represents a computation data flow and Σ-HCOL, which extends HCOL with iterative computations. A framework for automatically proving semantic preservation of expression rewriting for both languages is presented. The structural properties of the dataflow graph which allow efficient compilation are formalized, and a monadic approach to tracking them and to reasoning about structural correctness of Σ-HCOL expressions is presented.

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Modular Acceleration: Tricky Cases of Functional High-Performance Computing
Troels Henriksen, Martin Elsman, and Cosmin E. Oancea
(University of Copenhagen, Denmark)
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.

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