Providing performance portable numerics for Intel GPUs

TitleProviding performance portable numerics for Intel GPUs
Publication TypeJournal Article
Year of Publication2022
AuthorsTsai, Y-H. M., T. Cojean, and H. Anzt
JournalConcurrency and Computation: Practice and Experience
Date Published2022-10
KeywordsGinkgo, Intel GPUs, math library, oneAPI, SpMV

With discrete Intel GPUs entering the high-performance computing landscape, there is an urgent need for production-ready software stacks for these platforms. In this article, we report how we enable the Ginkgo math library to execute on Intel GPUs by developing a kernel backed based on the DPC++ programming environment. We discuss conceptual differences between the CUDA and DPC++ programming models and describe workflows for simplified code conversion. We evaluate the performance of basic and advanced sparse linear algebra routines available in Ginkgo's DPC++ backend in the hardware-specific performance bounds and compare against routines providing the same functionality that ship with Intel's oneMKL vendor library.

Short TitleConcurrency and Computation
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