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Verlag: Morgan Kaufmann Publishers, 2012
ISBN 10: 0124159338 ISBN 13: 9780124159334
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.3.
Verlag: Morgan Kaufmann Publishers, 2012
ISBN 10: 0124159338 ISBN 13: 9780124159334
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Brand New. 1st edition. 600 pages. 9.25x7.50x1.00 inches. In Stock.
Verlag: Morgan Kaufmann 2012-11-13, 2012
ISBN 10: 0124159338 ISBN 13: 9780124159334
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New.
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In den Warenkorbpaperback. Zustand: Gut. 590 Seiten; 9780124159334.3 Gewicht in Gramm: 2.
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In den Warenkorbpaperback. Zustand: New. Paperback. Pub Date :2014-01-01 Pages: 522 Language: Chinese Publisher: China Machine Press High Performance Computing Series CUDA parallel programming: GPU Programming Guide is the CUDA parallel programming areas most comprehensive. detailed and One of the most authoritative books. personally written by the developer community. technical director CUDA. NVIDIA CUDA China's first official translation certified engineers. detailed to explain the CUDA parallel programming technology knowledge (p.
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EUR 55,74
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In den WarenkorbTaschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems. Comprehensive introduction to parallel programming with CUDA, for readers new to both Detailed instructions help readers optimize the CUDA software development kit Practical techniques illustrate working with memory, threads, algorithms, resources, and more Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets Each chapter includes exercises to test reader knowledge.