In the previous episode of CUDACasts I introduced you to NumbaPro, the high-performance Python compiler from Continuum Analytics, and demonstrated how to accelerate simple Python functions on the GPU. Continuing the Python theme, today’s CUDACast demonstrates NumbaPro’s support for CUDA libraries.
The optimized algorithms in GPU-accelerated libraries often provide the easiest way to accelerate applications. NumbaPro includes a Python API interface to the cuBLAS, cuFFT, and cuRAND libraries. In CUDACasts episode #11 I show you how to use cuRAND to accelerate random-number generation for a Python Monte Carlo options pricing example, achieving a 17x overall speed-up.
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