GUM

Author:

Trinder P. W.1,Hammond K.1,Mattson J. S.2,Partridge A. S.3,Peyton Jones S. L.1

Affiliation:

1. Department of Computing Science, Glasgow University

2. Hewlett-Packard, California Language Laboratory and Department of Computing Science, Glasgow University

3. Department of Computer Science, University of Tasmania and Department of Computing Science, Glasgow University

Abstract

GUM is a portable, parallel implementation of the Haskell functional language. Despite sustained research interest in parallel functional programming, GUM is one of the first such systems to be made publicly available.GUM is message-based, and portability is facilitated by using the PVM communications harness that is available on many multi-processors. As a result, GUM is available for both shared-memory (Sun SPARCserver multiprocessors) and distributed-memory (networks of workstations) architectures. The high message-latency of distributed machines is ameliorated by sending messages asynchronously, and by sending large packets of related data in each message.Initial performance figures demonstrate absolute speedups relative to the best sequential compiler technology. To improve the performance of a parallel Haskell program GUM provides tools for monitoring and visualising the behaviour of threads and of processors during execution.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 26 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reliability and Security of Extreme Parallelism;IEEE Consumer Electronics Magazine;2021

2. A Semantic Framework to Debug Parallel Lazy Functional Languages;Mathematics;2020-05-26

3. Colocation of Potential Parallelism in a Distributed Adaptive Run-Time System for Parallel Haskell;Lecture Notes in Computer Science;2019

4. PorcE: a deparallelizing compiler;Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes - MPLR 2019;2019

5. And Now for Something Completely Different: Running Lisp on GPUs;2018 IEEE International Conference on Cluster Computing (CLUSTER);2018-09

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