Evaluation of Inter- and Intra-node Data Transfer Efficiencies between GPU Devices and their Impact on Scalable Applications

Data movement is of high relevance for GPU Computing. Communication and performance efficiencies of applications and systems with GPU accelerators depend on on- and off-node data paths, thereby making tuning and optimization an increasingly complex task. In this paper we conduct an in-depth study to establish the parameters that influence performance of data transfers between on-node GPU devices, and located on separate nodes (off-node). We compare the most recent version of MVAPICH2 featuring seamless remote GPU transfers with our own low-level benchmarks, and discuss the bottlenecks that may arise. Data path performance and bottlenecks between GPU devices are analyzed and compared for two substantially different systems: an IBM datable relying on an InfiniBand QDR fabric with two on-node GPU devices, and a Cray XK6, featuring a single GPU per node, and connected through a Gemini interconnect. Finally, we adapt LAMMPS, a GPU-accelerated application, to benefit from efficient inter-GPU data transfers, and validate our findings.