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  • Ký hiệu PL/XG: 005.275 S7314
    Nhan đề: GPU parallel program development using CUDA /
ISBN 9780367572242
DDC 005.275
Tác giả CN Soyata, Tolga
Nhan đề GPU parallel program development using CUDA / Tolga Soyata
Lần xuất bản First edition
Thông tin xuất bản Boca Raton, FL : CRC Press, 2018.
Mô tả vật lý 440 pages. : illustrations ; 25 cm.
Tóm tắt "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."
Thuật ngữ chủ đề Parallel programming (Computer science)
Thuật ngữ chủ đề CUDA (Computer architecture)
Thuật ngữ chủ đề Graphics processing units-Programming.
Khoa Khoa Công nghệ Thông tin
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100 |aSoyata, Tolga|d1967-
245 |aGPU parallel program development using CUDA / |cTolga Soyata
250 |aFirst edition
260 |aBoca Raton, FL : |bCRC Press, |c2018.
300 |a440 pages. : |billustrations ; |c25 cm.
504 |aIncludes bibliographical references (pages 435-436) and index.
520 |a"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."
541 |aMua
650 |aParallel programming (Computer science)
650 |aCUDA (Computer architecture)
650 |aGraphics processing units|xProgramming.
690 |aKhoa Công nghệ Thông tin
852|a300|bQ12_Kho Mượn_02|j(1): 072488
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