  With the announcement that the Aurora Supercomputer will be composed of
general purpose Intel CPUs complemented by discrete high performance Intel
GPUs, and the deployment of the oneAPI ecosystem, Intel has committed to enter
the arena of discrete high performance GPUs. A central requirement for the
scientific computing community is the availability of production-ready software
stacks and a glimpse of the performance they can expect to see on Intel high
performance GPUs. In this paper, we present the first platform-portable open
source math library supporting Intel GPUs via the DPC++ programming
environment. We also benchmark some of the developed sparse linear algebra
functionality on different Intel GPUs to assess the efficiency of the DPC++
programming ecosystem to translate raw performance into application
performance. Aside from quantifying the efficiency within the hardware-specific
roofline model, we also compare against routines providing the same
functionality that ship with Intel's oneMKL vendor library.
