@article {,
	title = {Providing performance portable numerics for Intel GPUs},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {17},
	year = {2022},
	month = {2022-10},
	abstract = {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{\textquoteright}s DPC++ backend in the hardware-specific performance bounds and compare against routines providing the same functionality that ship with Intel{\textquoteright}s oneMKL vendor library.},
	keywords = {Ginkgo, Intel GPUs, math library, oneAPI, SpMV},
	issn = {1532-0626},
	doi = {10.1002/cpe.7400},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.7400},
	author = {Tsai, Yu-Hsiang M. and Cojean, Terry and Anzt, Hartwig}
}
