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Symbolic parallelization of nested l...

Hannig, Frank.

Symbolic parallelization of nested loop programs

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Symbolic parallelization of nested loop programsby Alexandru-Petru Tanase, Frank Hannig, Jurgen Teich.
作者:	Tanase, Alexandru-Petru.
其他作者:	Hannig, Frank.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xii, 176 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Parallel programming (Computer science)
電子資源:	http://dx.doi.org/10.1007/978-3-319-73909-0
ISBN:	9783319739090$q(electronic bk.)

Symbolic parallelization of nested loop programs
Tanase, Alexandru-Petru.

Symbolic parallelization of nested loop programs[electronic resource] /by Alexandru-Petru Tanase, Frank Hannig, Jurgen Teich. - Cham :Springer International Publishing :2018. - xii, 176 p. :ill., digital ;24 cm.

Introduction -- Fundamentals and Compiler Framework -- Symbolic Parallelization -- Symbolic Multi-level Parallelization -- On-demand Fault-tolerant Loop Processing -- Conclusions.

This book introduces new compilation techniques, using the polyhedron model for the resource-adaptive parallel execution of loop programs on massively parallel processor arrays. The authors show how to compute optimal symbolic assignments and parallel schedules of loop iterations at compile time, for cases where the number of available cores becomes known only at runtime. The compile/runtime symbolic parallelization approach the authors describe reduces significantly the runtime overhead, compared to dynamic or just-in-time compilation. The new, on-demand fault-tolerant loop processing approach described in this book protects loop nests for parallel execution against soft errors.

ISBN: 9783319739090$q(electronic bk.)

Standard No.: 10.1007/978-3-319-73909-0doiSubjects--Topical Terms:

215285
Parallel programming (Computer science)

LC Class. No.: QA76.642

Dewey Class. No.: 005.275

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520 $a This book introduces new compilation techniques, using the polyhedron model for the resource-adaptive parallel execution of loop programs on massively parallel processor arrays. The authors show how to compute optimal symbolic assignments and parallel schedules of loop iterations at compile time, for cases where the number of available cores becomes known only at runtime. The compile/runtime symbolic parallelization approach the authors describe reduces significantly the runtime overhead, compared to dynamic or just-in-time compilation. The new, on-demand fault-tolerant loop processing approach described in this book protects loop nests for parallel execution against soft errors.
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