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Ben Perry

dblp:05/8524 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

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

Systems, architecture and hardware · 1 · 1 first-author

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel programming models › message passing
MPI communication
0.112010
Improving MPI communication via data type fission · HPDC 2010
Parallel and multicore computing
SPMD applications
0.012010
Improving MPI communication via data type fission · HPDC 2010

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

data type fission · 0.1
YearPublicationVenuePosition
2010 Improving MPI communication via data type fission
abstract
Message Passing Interface (MPI) messages are centered around transmitting instances of MPI data types. The data types represented in MPI terms are usually modeled after data types native to the application. If a user does not want to transmit a field from the native data type, the user will sometimes align the MPI data type such that there is a gap in the displacement where the omitted field would be. With the resulting MPI data type now being non-contiguous, cycles are spent making the data contiguous for transmission and then expanding the data back out on the receiving side. We show that by performing data type fission (the process of segregating the transmitted fields from the non-transmitted fields) and aligning the MPI data type accordingly, we can completely eliminate the need to copy data during the packing and unpacking process, which can significantly improve the performance of communication-heavy Single Program Multiple Data applications.
Ben Perry, D. Martin Swany
HPDC1