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CUDA Application Design and Development by Rob Farber

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Chapter 2. CUDA for Machine Learning and Optimization
GPGPUs are powerful tools that are well-suited to unraveling complex real-world problems. Using only the simple CUDA capabilities introduced in Chapter 1, this chapter demonstrates how to greatly accelerate nonlinear optimization problems using the derivative-free Nelder-Mead and Levenberg-Marquardt optimization algorithms. Single- and double-precision application performance will be measured and compared between an Intel Xeon e5630 processor and an NVIDIA C2070 GPU as well as an older 10-series NVIDIA GTX 280 gaming GPU. Working example code is provided that can train the classic nonlinear XOR machine-learning problem 85 times faster than a modern quad-core Intel Xeon processor (341 times ...

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