To take advantage of distributed-memory parallel machines it is essential to have good control of task granularity. This paper describes a fairly accurate parallel simulator for Haskell, based on the Glasgow compiler, and complementary tools for visualising task granularities. Together these tools allow us to study the effects of various annotations on task granularity on a variety of simulated parallel architectures. They also provide a more precise tool for the study of parallel execution than has previously been available for Haskell programs. These tools have already confirmed that thread migration is essential in parallel systems, demonstrated a close correlation between thread execution times and total heap allocations, and shown that fetching data synchronously normally gives better overall performance than asynchronous fetching, if data is fetched on demand.
@InProceedings{winnowing,
author = {Kevin Hammond and Hans-Wolfgang Loidl and Andrew Partridge},
title = {Visualising Granularity in Parallel Programs: A Graphical Winnowing System for Haskell},
booktitle = {Conference on High Performance Functional Computing},
year = {1995},
address = {Denver, Colorado},
month = apr,
pages = {208--221}
}