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's oneMKL vendor library.