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EUR 71,89
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Añadir al carritoPaperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 61,59
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EUR 69,86
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Añadir al carritoPaperback. Condición: Brand New. 476 pages. 10.00x7.01x1.10 inches. In Stock.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 64,03
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 65,01
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Añadir al carritoCondición: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
EUR 72,29
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 881.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 71,83
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 72,40
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EUR 76,98
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - This book teaches GPU programming by introducing CPU multi-threaded programming and bases GPU massively-parallel programming on this foundation. The differences among families of GPUs are also studied. The book also explores CUDA libraries, OpenCL, GPU programming with other languages and API libraries, and the deep learning library cuDNN.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 84,20
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Añadir al carritoCondición: New. pp. 476.
EUR 77,46
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Añadir al carritoCondición: New. pp. 476.
Publicado por Chapman and Hall/CRC 2016-10-06, 2016
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: Chiron Media, Wallingford, Reino Unido
EUR 81,85
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Añadir al carritoHardcover. Condición: New.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 82,29
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Añadir al carritoCondición: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 84,88
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EUR 94,36
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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days. 1078.
Publicado por Taylor & Francis Group, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: Majestic Books, Hounslow, Reino Unido
EUR 98,91
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 92,51
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 92,49
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EUR 92,62
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Añadir al carritoGebunden. Condición: New. Tolga Soyata is an associate professor in the Electrical and Computer Engineering department of SUNY Albany.GPU Parallel Program Development using CUDA teaches GPU programming by showing the diffe.
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EUR 84,69
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Añadir al carritoCondición: Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Añadir al carritoHardcover. Condición: Brand New. 440 pages. 10.00x7.00x1.25 inches. In Stock.
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EUR 100,12
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Publicado por Taylor & Francis Group, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 115,96
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Publicado por Taylor & Francis Feb 2018, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 115,30
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Añadir al carritoBuch. Condición: Neu. Neuware.
Publicado por Taylor & Francis Group, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 118,45
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Librería: SGS Trading Inc, Franklin Lakes, NJ, Estados Unidos de America
EUR 117,98
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Añadir al carritohardcover. Condición: Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking.
Publicado por Taylor & Francis Inc, Portland, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 102,52
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Añadir al carritoHardcover. Condición: new. Hardcover. GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation.Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs.Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apples Swift and Metal,) and the deep learning library cuDNN. This book teaches GPU programming by introducing CPU multi-threaded programming and bases GPU massively-parallel programming on this foundation. The differences among families of GPUs are also studied. The book also explores CUDA libraries, OpenCL, GPU programming with other languages and API libraries, and the deep learning library cuDNN. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Taylor & Francis Inc, Portland, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 148,24
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Añadir al carritoHardcover. Condición: new. Hardcover. GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation.Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs.Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apples Swift and Metal,) and the deep learning library cuDNN. This book teaches GPU programming by introducing CPU multi-threaded programming and bases GPU massively-parallel programming on this foundation. The differences among families of GPUs are also studied. The book also explores CUDA libraries, OpenCL, GPU programming with other languages and API libraries, and the deep learning library cuDNN. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: moluna, Greven, Alemania
EUR 56,83
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tolga Soyata is an associate professor in the Electrical and Computer Engineering department of SUNY Albany.GPU Parallel Program Development using CUDA teaches GPU programming by showing th.