Abstract-Large-scale GPU clusters are gaining popularity in the scientific computing community. However, their deployment and production use are associated with a number of new challenges. In this paper, we present our efforts to address some of the challenges with building and running GPU clusters in HPC environments. We touch upon such issues as balanced cluster architecture, resource sharing in a cluster environment, programming models, and applications for GPU clusters.

No abstract

We present an inexpensive hardware system for monitoring power usage of individual CPU hosts and externally attached GPUs in HPC clusters and the software stack for integrating the power usage data streamed in realtime by the power monitoring hardware with the cluster management software tools. We introduce a measure for quantifying the overall improvement in performance-per-watt for applications that have been ported to work on the GPUs. We use the developed hardware/software infrastructure to demonstrate the overall improvement in performance-per-watt for several HPC applications implemented to work on GPUs.