Ginkgo: An Open Source Math Library in the oneAPI Ecosystem
Ginkgo is an open source math library designed for GPU-accelerated supercomputers. In this talk, we present the path we took to prepare Ginkgo for Intel® GPUs. We start with reporting our experiences in porting the NVIDIA*-focused software stack to Intel's DPC++ environment and the obstacles we encountered when using automated code conversion. Then we present the functionality that Ginkgo currently provides for Intel GPUs, and the performance we achieve for key linear algebra building blocks on recent Intel GPUs. We conclude by demonstrating how Ginkgo's DPC++ backend can be used to prepare scientific applications for the oneAPI ecosystem.
Hartwig Anzt is a research group leader at the Steinbuch Centre for Computing at the Karlsruhe Institute of Technology (KIT). He obtained his PhD in Mathematics at the Karlsruhe Institute of Technology. Afterward, he joined Jack Dongarra's Innovative Computing Lab at the University of Tennessee in 2013 until he started his own research group in 2017. He still contributed to the Innovative Computing Lab as a research consultant. Hartwig Anzt has a strong background in numerical mathematics, and specializes in iterative methods and preconditioning techniques for the next-generation hardware architectures. His Helmholtz group on fixed-point methods for numerics at Exascale (FiNE) has been granted funding until 2022. Hartwig Anzt has a long track record of high-quality software development. He is the author of the MAGMA-sparse open source software package and managing lead of the Ginkgo numerical linear algebra library. Hartwig Anzt is PI of the EuroHPC project MICROCARD, and a co-PI of the PEEKS project and the xSDK project inside the software technology effort of the US Exascale Computing Project (ECP). He is also the technical PI of the multiprecision effort in the xSDK project, a coordinated effort that aims to integrate low-precision functionality into high-accuracy simulation codes.
- Article: Prepare for the Arrival of Intel’s Discrete High-Performance GPUs
- Article: Porting a Sparse Linear Algebra Math Library to Intel GPUs
- Learn More about oneAPI
- Try oneAPI in a Preconfigured Environment with Intel® DevCloud
- Download oneAPI Toolkits and Experiment on Your Own
- Watch Past oneAPI Developer Summit Videos and Find Upcoming Events
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.