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Author : Dr. Brian Tuomanen
Date : 2018-11-27
Page : 312
Rating : 5.0
Reviews : 4
Category : Book
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HandsOn GPU Programming with Python and CUDA Explore ~ HandsOn 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
HandsOn GPU Programming with Python and CUDA Explore ~ HandsOn GPU Programming with Python and CUDA will help you discover ways to develop high performing Python apps combining the power of Python and CUDA This book will help you hit the ground runningyoull start by learning how to apply Amdahls law use a code profiler to identify bottlenecks in your Python code and set up a GPU programming environment
HandsOn GPU Programming with Python and CUDA Explore ~ HandsOn GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code
HandsOn GPU Programming with Python and CUDA GitHub ~ HandsOn GPU Programming with Python and CUDA This is the code repository for HandsOn GPU Programming with Python and CUDA published by Packt Explore highperformance parallel computing with CUDA What is this book about
HandsOn GPU Programming with Python and CUDA Book ~ HandsOn GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code
HandsOn GPU Computing with Python ~ Explore GPUenabled programmable environment for machine learning GPUs are proving to be excellent general purposeparallel computing solutions for high performance tasks such as deep learning and scientific computing HandsOn GPU Programming with Python and CUDA 720 RRP 3599 Save 80 eBook
HandsOn GPU Computing with Python Explore the ~ HandsOn GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems GPU computing and explain the architecture and programming models for GPUs You will learn by example how to perform GPU programming with Python and you’ll look at using integrations such as PyCUDA PyOpenCL CuPy
HandsOn GPU Programming with Python and CUDA ~ We suggest the use of Python 27 over Python 3x since Python 27 has stable support across all the libraries we use in this book HandsOn GPU Programming with Python and CUDA JavaScript seems to be disabled in your browser
HandsOn GPU Computing with Python » Free books EPUB ~ • Utilize Python libraries and frameworks for GPU acceleration • Set up a GPUenabled programmable machine learning environment on your system with Anaconda • Deploy your machine learning system on cloud containers with illustrated examples • Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA OpenCL and ROCm
HandsOn GPU Programming with Python and CUDA ~ HandsOn 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
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