A Framework for Collective Personalized Communication
IEEE International Parallel and Distributed Processing Symposium (IPDPS) 2003
Publication Type: Paper
Repository URL: CommLib
Abstract
This paper explores collective personalized communication. For
example, in all-to-all personalize d communication (AAPC), each
processor sends a distinct message to every other processor.
However, for many applica tions, the collec- tive communication
pattern is many-to-many, where each processor sends a distinct
message to a subs et of processors. In this paper we first present
strategies that reduce per-message cost to optimize AAPC. We then
prese nt performance results of these strategies in both all-to-all
and many-to-many scenarios. These strategies are implemented in a
flexible, asynchronous library with a non-blocking interface, and a
message-driven runtime system. This allows the collect ive
communication to run concurrently with the application, if desired.
As a result the computational overhead of the commun ication is
substantially reduced, at least on machines such as PSC Lemieux,
which sport a co-processor capable of remote DMA . We demonstrate
the advantages of our framework with performance results on several
benchmarks and applications.
TextRef
L. V. Kale and Sameer Kumar and Krishnan Vardarajan,
"A Framework for Collective Personalized Communication, Communicated to IPDPS 2003",
Parallel Programming Laboratory, Department of Computer Science,
University of Illinois at Urbana-Champaign, 2002.
People
Research Areas