Accelerated high-performance computing through efficient multi-process GPU resource sharing
Proceedings of the 9th conference on Computing Frontiers
Fine-grained resource sharing for concurrent GPGPU kernels
HotPar'12 Proceedings of the 4th USENIX conference on Hot Topics in Parallelism
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Modern Graphic Processing Units (GPUs) are widely used as application accelerators in the High Performance Computing (HPC) field due to their massive floating-point computational capabilities and highly data-parallel computing architecture. Contemporary high performance computers equipped with co-processors such as GPUs primarily execute parallel applications using the Single Program Multiple Data (SPMD) model, which requires balanced computing resources of both microprocessor and co-processors to ensure full system utilization. While the inclusion of GPUs in HPC systems provides more computing resources and significant performance improvements, the asymmetrical distribution of the number of GPUs relative to the microprocessors can result in an underutilization of overall system computing resources. In this paper, we propose a GPU resource virtualization approach to allow underutilized microprocessors to share the GPUs. We analyze factors affecting the parallel execution performance on GPUs and conduct a theoretical performance estimation based on the most recent GPU architectures as well as the SPMD model. Then we present the implementation details of the virtualization infrastructure, followed by an experimental verification of the proposed concepts using an NVIDIA Fermi GPU computing node. The results demonstrate a considerable performance gain over the traditional SPMD execution without virtualization. Furthermore, the proposed solution enables full utilization of the asymmetrical system resources, through the sharing of the GPUs among microprocessors, while incurring low overheads due to the virtualization layer.