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. 1 Introduction Our aim is to produce fast, cost-effective implementations of lazy functional languages. One way to impro...

This paper assesses the costs of maintaining a virtual shared heap in our parallel graph reducer (GUM), which implements a parallel functional language. GUM performs automatic and dynamic resource management for both work and data. We introduce extensions to the original design of GUM, aiming at a more flexible memory management and communication mechanism to deal with high-latency systems. We then present measurements of running GUM on a Beowulf cluster, evaluating the overhead of dynamic distributed memory management and the effectiveness of the new memory management and communication mechanisms. c IEEE; "CCGrid 2002", Berlin, May 2002.