CUDACasts_FeaturedImage

CUDACasts Episode #11: GPU Libraries for CUDA Python

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.

To request a topic for a future episode of CUDACasts, or if you have any other feedback, please use the contact form or leave a comment to let us know.

∥∀

About Mark Ebersole

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University. Follow @cudahamster on Twitter