  NVIDIA has been making steady progress in increasing the compute performance
of its GPUs, resulting in order of magnitude compute throughput improvements
over the years. With several models of GPUs coexisting in many deployments, the
traditional accounting method of treating all GPUs as being equal is not
reflecting compute output anymore. Moreover, for applications that require
significant CPU-based compute to complement the GPU-based compute, it is
becoming harder and harder to make full use of the newer GPUs, requiring
sharing of those GPUs between multiple applications in order to maximize the
achievable science output. This further reduces the value of whole-GPU
accounting, especially when the sharing is done at the infrastructure level. We
thus argue that GPU accounting for throughput-oriented infrastructures should
be expressed in GPU core hours, much like it is normally done for the CPUs.
While GPU core compute throughput does change between GPU generations, the
variability is similar to what we expect to see among CPU cores. To validate
our position, we present an extensive set of run time measurements of two
IceCube photon propagation workflows on 14 GPU models, using both on-prem and
Cloud resources. The measurements also outline the influence of GPU sharing at
both HTCondor and Kubernetes infrastructure level.
