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With AMD reinforcing their ambition in the scientific high performancecomputing ecosystem, we extend the hardware scope of the Ginkgo linear algebrapackage to feature a HIP backend for AMD GPUs. In this paper, we report anddiscuss the porting effort from CUDA, the extension of the HIP framework to addmissing features such as cooperative groups, the performance price of compilingHIP code for AMD architectures, and the design of a library providing nativebackends for NVIDIA and AMD GPUs while minimizing code duplication by using ashared code base.

Preparing Ginkgo for AMD GPUs -- A Testimonial on Porting CUDA Code to HIP