  With AMD reinforcing their ambition in the scientific high performance
computing ecosystem, we extend the hardware scope of the Ginkgo linear algebra
package to feature a HIP backend for AMD GPUs. In this paper, we report and
discuss the porting effort from CUDA, the extension of the HIP framework to add
missing features such as cooperative groups, the performance price of compiling
HIP code for AMD architectures, and the design of a library providing native
backends for NVIDIA and AMD GPUs while minimizing code duplication by using a
shared code base.
