Development and Optimization of a SYCL Backend for libCEED
libCEED is a new library, developed as part of the Exascale Computing Project (ECP), that provides a highly efficient and portable interface for high-order finite element implementations that can be easily integrated with a wide variety of scientific software based on discretization. The libCEED library provides high-performance, GPU specific implementations using CUDA, HIP, and MAGMA backends that can be chosen at runtime by the source application. In this talk, we describe our experiences and challenges encountered in developing and optimizing the SYCL backend implementations for the libCEED library. We will focus on the basic software design of the library, SYCL programming choices made during the development, and leveraging some key SYCL programming capabilities, such as reductions, specialization constants, kernel bundles, as well as the Intel extension for SYCL online compilation provided within the oneAPI software toolkit.
Speaker(s)
Kris Rowe - Argonne National Laboratory/Assistant Computational Scientist
Kris is an Assistant Computational Scientist at Argonne National Laboratory’s Leadership Computing Facility. An applied mathematician by training, Kris holds a PhD from the University of Waterloo—with research focusing on geophysical fluid dynamics, high-order methods for incompressible flows, and adaptive mesh refinement. During his postdoc at Cornell University, he went undercover in the Civil and Environmental Engineering Department to study internal waves radiated by the wakes of submersible vehicles, and design fast algorithms for high-order methods. Currently, as part of the Performance Engineering Group at ALCF, he is working to prepare computational science and engineering applications for the Aurora exascale supercomputer.
Varsha Madananth - Intel Corporation/Applications Engineer
Varsha Madananth is an Applications engineer at Intel. She has been working on enabling workload on PVC with a focus on SYCL. She also has experience in performance optimizations using parallel programming techniques like vectorization, threading and heterogenous compute, compiler optimizations, and micro-architecture tuning.
Umesh Unnikrishnan - Argonne National Laboratory/Postdoctoral Appointee
Dr. Umesh Unnikrishnan is a postdoctoral appointee in the Argonne National Laboratory. He has a background in computational fluid dynamics and high performance computing. His work at the Argonne Leadership Computing Facility focuses on the development of finite-element based software libraries for running multi-physics scientific codes on GPU computing platforms. Umesh holds a PhD in Aerospace Engineering from the Georgia Institute of Technology.
