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.
@InProceedings{Loid02,
author = {Loidl, H-W.},
title = {{The Virtual Shared Memory Performance of a Parallel Graph Reducer}},
booktitle = {{DSM 2002 --- International Workshop on Distributed Shared
Memory on Clusters}},
address = {Berlin, Germany},
month = may,
year = 2002,
note = {{Organised with CCGrid 2002 --- International Symposium
on Cluster Computing and the Grid}},
}