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Da: California Books, Miami, FL, U.S.A.
EUR 68,24
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Da: Majestic Books, Hounslow, Regno Unito
EUR 64,23
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New. pp. 476.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 61,85
Quantità: 10 disponibili
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 68,84
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Aggiungi al carrelloCondizione: New. In.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 476.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 68,61
Quantità: 10 disponibili
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 74,71
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Aggiungi al carrelloCondizione: New. pp. 476.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 68,63
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days. 881.
Condizione: Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Da: Majestic Books, Hounslow, Regno Unito
EUR 95,84
Quantità: 3 disponibili
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Condizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 89,36
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Revaluation Books, Exeter, Regno Unito
EUR 93,88
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Aggiungi al carrelloPaperback. Condizione: Brand New. 476 pages. 10.00x7.01x1.10 inches. In Stock.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 100,83
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 440 pages. 10.00x7.00x1.25 inches. In Stock.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 102,63
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 115,17
Quantità: 3 disponibili
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 104,05
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: moluna, Greven, Germania
EUR 92,62
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Aggiungi al carrelloGebunden. Condizione: 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.
Lingua: Inglese
Editore: Chapman And Hall/CRC Jun 2020, 2020
ISBN 10: 0367572249 ISBN 13: 9780367572242
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 59,60
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Apple's Swift and Metal,) and the deep learning library cuDNN. 478 pp. Englisch.
Da: moluna, Greven, Germania
EUR 56,83
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Aggiungi al carrelloCondizione: 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.
Lingua: Inglese
Editore: Taylor & Francis, Chapman And Hall/CRC, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 89,40
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Apple's Swift and Metal,) and the deep learning library cuDNN. 476 pp. Englisch.
Da: preigu, Osnabrück, Germania
EUR 58,15
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. GPU Parallel Program Development Using CUDA | Tolga Soyata | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367572242 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,33
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Apple's Swift and Metal,) and the deep learning library cuDNN.
Lingua: Inglese
Editore: Taylor & Francis, Chapman And Hall/CRC, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 103,11
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Apple's Swift and Metal,) and the deep learning library cuDNN.