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In den WarenkorbTaschenbuch. Zustand: 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.
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In den WarenkorbGebunden. Zustand: 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.
Verlag: Chapman and Hall/CRC 2016-10-06, 2016
ISBN 10: 1498750753 ISBN 13: 9781498750752
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Brand New. 440 pages. 10.00x7.00x1.25 inches. In Stock.
Verlag: Taylor & Francis Feb 2018, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Sprache: Englisch
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In den Warenkorbhardcover. Zustand: New. New Textbook, Ships with Tracking.
Verlag: Taylor & Francis Inc, Portland, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: 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.
Verlag: Taylor & Francis Inc, Portland, 2018
ISBN 10: 1498750753 ISBN 13: 9781498750752
Sprache: Englisch
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EUR 147,14
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In den WarenkorbHardcover. Zustand: 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.