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With the announcement that the Aurora Supercomputer will be composed ofgeneral purpose Intel CPUs complemented by discrete high performance IntelGPUs, and the deployment of the oneAPI ecosystem, Intel has committed to enterthe arena of discrete high performance GPUs. A central requirement for thescientific computing community is the availability of production-ready softwarestacks and a glimpse of the performance they can expect to see on Intel highperformance GPUs. In this paper, we present the first platform-portable opensource math library supporting Intel GPUs via the DPC++ programmingenvironment. We also benchmark some of the developed sparse linear algebrafunctionality on different Intel GPUs to assess the efficiency of the DPC++programming ecosystem to translate raw performance into applicationperformance. Aside from quantifying the efficiency within the hardware-specificroofline model, we also compare against routines providing the samefunctionality that ship with Intel's oneMKL vendor library.

Porting a sparse linear algebra math library to Intel GPUs