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    Hands-On GPU Programming with Python and CUDA. Explore high-performance parallel computing with CUDA

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Hands-On GPU Programming with Python and CUDA. Explore high-performance parallel computing with CUDA Dr. Brian Tuomanen - okładka ebooka

    Hands-On GPU Programming with Python and CUDA. Explore high-performance parallel computing with CUDA Dr. Brian Tuomanen - okładka ebooka

    Hands-On GPU Programming with Python and CUDA. Explore high-performance parallel computing with CUDA Dr. Brian Tuomanen - okładka audiobooka MP3

    Hands-On GPU Programming with Python and CUDA. Explore high-performance parallel computing with CUDA Dr. Brian Tuomanen - okładka audiobooks CD

    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    310
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    Ebook

    139,00 zł

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    Do przechowalni

    Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.

    As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.

    With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.

    By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.

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    O autorze ebooka

    Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.

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