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Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
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Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. 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
- Apply GPU programming to modern data science applications
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