Develop for NVIDIA* GPUs Using SYCL* with Intel oneAPI: Part 1

Support from the community for SYCL* is growing, with some of the most powerful supercomputers in the world (including Aurora, Perlmutter, and Frontier) adopting the programming model for cutting-edge research. By migrating your code from CUDA* to SYCL, it's not only possible to still target NVIDIA* GPUs, but it's also possible to deploy to a wider set of GPUs from different companies including Intel and AMD*.

This hands-on workshop introduces the basics of how to set up your development environment to use SYCL to target NVIDIA GPUs using oneAPI and what you need to know to migrate your code from CUDA to SYCL. Find out how to use incremental porting by using interoperability for native CUDA kernel code and libraries and learn the fundamentals needed to get the full performance with SYCL. In addition, learn how you can call CUDA libraries such as cuDNN or cuBLAS directly, or via existing SYCL libraries such as oneDNN using oneAPI for CUDA.

Joe Todd is a senior software engineer at Codeplay* with a decade of experience developing parallel software. Joe's career began with a PhD in glaciology at the University of Cambridge during which he implemented an ice-fracture extension to the parallel finite element model, Elmer FEM. Subsequently, he spent several years as a post-doctoral researcher at the University of St Andrews and the University of Edinburgh developing a massively-parallel, particle-based fracture model and a Bayesian framework for uncertainty propagation through ice sheet models. Most recently, driven by a desire to focus on software engineering, Joe left academia to join Codeplay, where he works on SYCL's CUDA back end, with a continuing focus on particle simulators.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.