Dr. Brian Tuomanen创作的计算机网络小说《Hands-On GPU Programming with Python and CUDA》,已更新0章,最新章节:。Hands-OnGPUProgrammingwithPythonandCUDAhitsthegroundrunning:you’llstartbylearninghowtoapplyAmdahl’sLaw,useacodeprofilert...
We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key Features Expand your background in GPU programming - PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver...
but are subject to low-level subtleties. In contrast, Python is a high-level language that places emphasis on ease-of-use over speed. This updated second edition follows a practical approach to teaching you efficient GPU programming techniques with the latest version of Python and CUDA. ...
Hands-On GPU Programming with Python and CUDA是Dr. Brian Tuomanen创作的计算机网络类小说,QQ阅读提供Hands-On GPU Programming with Python and CUDA部分章节免费在线阅读,此外还提供Hands-On GPU Programming with Python and CUDA全本在线阅读。
So, now we finally completed our first true GPU program of the text! (Also available athttps://github.com/PacktPublishing/Hands-On-GPU-Programming-with-Python-and-CUDA/blob/master/3/deviceQuery.py). Now, we can run it as follows:
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 t
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 ...
In PyCUDA, GPU code is often compiled at runtime with the NVIDIA nvcc compiler and then subsequently called from PyCUDA. This can lead to an unexpected slowdown, usually the first time a program or GPU operation is run in a given Python session. 目录...
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