A framework for exploiting task and data parallelism on distributed memory multicomputers

Distributed Memory Multicomputers (DMMs), such as the IBM SP-2, the Intel Paragon, and the Thinking Machines CM-5, offer significant advantages over shared memory multiprocessors in terms of cost and scalability. Unfortunately, the utilization of all the available computational power in these machin...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on parallel and distributed systems Ročník 8; číslo 11; s. 1098 - 1116
Hlavní autoři: Ramaswamy, S., Sapatnekar, S., Banerjee, P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.11.1997
Témata:
Concurrent computing
Costs
Data structures
Distributed computing
Optimizing compilers
Parallel processing
Program processors
Random access memory
Runtime
Scalability
ISSN:1045-9219
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Distributed Memory Multicomputers (DMMs), such as the IBM SP-2, the Intel Paragon, and the Thinking Machines CM-5, offer significant advantages over shared memory multiprocessors in terms of cost and scalability. Unfortunately, the utilization of all the available computational power in these machines involves a tremendous programming effort on the part of users, which creates a need for sophisticated compiler and run-time support for distributed memory machines. In this paper, we explore a new compiler optimization for regular scientific applications-the simultaneous exploitation of task and data parallelism. Our optimization is implemented as part of the PARADIGM HPF compiler framework we have developed. The intuitive idea behind the optimization is the use of task parallelism to control the degree of data parallelism of individual tasks. The reason this provides increased performance is that data parallelism provides diminishing returns as the number of processors used is increased. By controlling the number of processors used for each data parallel task in an application and by concurrently executing these tasks, we make program execution more efficient and, therefore, faster.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1045-9219
DOI:10.1109/71.642945