@inproceedings {,
	title = {Porting Sparse Linear Algebra to~Intel GPUs},
	journal = {Euro-Par 2021: Parallel Processing Workshops},
	volume = {13098},
	year = {2022},
	month = {2022-06},
	pages = {57 - 68},
	publisher = {Springer International Publishing},
	address = {Lisbon, Portugal},
	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 paper, we report how we prepare the Ginkgo math library for Intel GPUs by developing a kernel backed based on the DPC++ programming environment. We discuss conceptual differences to the CUDA and HIP programming models and describe workflows for simplified code conversion. We benchmark advanced sparse linear algebra routines utilizing the converted kernels to assess the efficiency of the DPC++ backend in the hardware-specific performance bounds. We compare the performance of basic building blocks against routines providing the same functionality that ship with Intel{\textquoteright}s oneMKL vendor library.},
	keywords = {Ginkgo, Intel GPUs, math library, oneAPI, SpMV},
	isbn = {978-3-031-06155-4},
	doi = {10.1007/978-3-031-06156-1_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-06156-1_5},
	author = {Tsai, Yuhsiang M. and Cojean, Terry and Anzt, Hartwig},
	editor = {Chaves, Ricardo and B. Heras, Dora and Ilic, Aleksandar and Unat, Didem and Badia, Rosa M. and Bracciali, Andrea and Diehl, Patrick and Dubey, Anshu and Sangyoon, Oh and L. Scott, Stephen and Ricci, Laura}
}
