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.