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James S. Mattson Jr.

dblp:92/3316 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel programming runtimes
message-driven runtime
0.011996
GUM: A Portable Parallel Implementation of Haskell · PLDI 1996
Parallel and multicore computing › parallel computing › parallel programming languages
parallel functional programming
0.011996
GUM: A Portable Parallel Implementation of Haskell · PLDI 1996
Parallel and multicore computing
parallel programming runtimes
0.011996
GUM: A Portable Parallel Implementation of Haskell · PLDI 1996

Methods — techniques the papers use, named apart from their topics

message passing · 0.0PVM · 0.0
YearPublicationVenuePosition
1996 GUM: A Portable Parallel Implementation of Haskell
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.
Philip W. Trinder, Kevin Hammond, James S. Mattson Jr., Andrew S. Partridge, Simon L. Peyton Jones
PLDI3
1990 Efficient Data-Driven Evaluation: Theory and Implementation
Harrick M. Vin, Francine Berman, James S. Mattson Jr.
J. Parallel Distributed Comput.3