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Compiling Parallel Loops for High Performance Computers : Partitioning, Data Assignment and Remapping - David E. Hudak

Compiling Parallel Loops for High Performance Computers

Partitioning, Data Assignment and Remapping

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Published: 31st October 1992
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The exploitationof parallel processing to improve computing speeds is being examined at virtually all levels of computer science, from the study of parallel algorithms to the development of microarchitectures which employ multiple functional units. The most visible aspect of this interest in parallel processing is the commercially available multiprocessor systems which have appeared in the past decade. Unfortunately, the lack of adequate software support for the development of scientific applications that will run efficiently on multiple processors has stunted the acceptance of such systems.
One of the major impediments to achieving high parallel efficiency on many data-parallel scientific applications is communication overhead, which is exemplified by cache coherency traffic and global memory overhead of interprocessors with a logically shared address space and physically distributed memory. Such techniques can be used by scientific application designers seeking to optimize code for a particular high-performance computer. In addition, these techniques can be seen as a necesary step toward developing software to support efficient paralled programs.
In multiprocessor sytems with physically distributed memory, reducing communication overhead involves both data partitioning and data placement. Adaptive Data Partitioning (ADP) reduces the execution time of parallel programs by minimizing interprocessor communication for iterative data-parallel loops with near-neighbor communication. Data placement schemes are presented that reduce communication overhead. Under the loop partition specified by ADP, global data is partitioned into classes for each processor, allowing each processor to cache certain regions of the global data set. In addition, for many scientific applications, peak parallel efficiency is achieved only when machine-specific tradeoffs between load imbalance and communication are evaluated and utilized in choosing the data partition. The techniques in this book evaluate these tradeoffs to generate optimum cyclic partitions for data-parallel loops with either a linearly varying or uniform computational structure and either neighborhood or dimensional multicast communication patterns. This tradeoff is also treated within the CPR (Collective Partitioning and Remapping) algorithm, which partitions a collection of loops with various computational structures and communication patterns. Experiments that demonstrate the advantage of ADP, data placement, cyclic partitioning and CPR were conducted on the Encore Multimax and BBN TC2000 multiprocessors using the ADAPT system, a program partitioner which automatically restructures iterative data-parallel loops.
This book serves as an excellent reference and may be used as the text for an advanced course on the subject.

List of Figures
List of Tables
Preface
Introduction
Contiguous Loop Partitions for Neighborhood Communication
Contiguous Data Assignments for Neighborhood Communication
Cyclic Loop Partitions for Linearly Varying Loops
Collective Partitioning and Remapping for Multiple Loop Nests
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780792392835
ISBN-10: 0792392833
Series: KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 159
Published: 31st October 1992
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 24.77 x 16.51  x 1.91
Weight (kg): 0.48