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The use of graphics processing units (GPUs) to accelerate some portions of applications is widespread nowadays. To avoid the usual inconveniences associated with these accelerators (high acquisition cost, high energy consumption, and low utilization), one possible solution is sharing them among several nodes in the cluster. Several years ago, remote GPU virtualization middleware systems appeared to implement this solution. Although these systems tackled the aforementioned inconveniences, their performance was usually impaired by the low bandwidth attained by the underlying network. However, the recent advances in InfiniBand fabrics have changed this trend. In this paper we analyze how the high bandwidth provided by the new EDR 100G Infini-Band fabric allows remote GPU virtualization middleware systems not only to perform very similar to local GPUs, but also to improve overall performance for some applications.
Abstract-Using GPUs reduces execution time of many applications but increases acquisition cost and power consumption. Furthermore, GPUs usually attain a relatively low utilization. In this context, remote GPU virtualization solutions were recently created to overcome the drawbacks of using GPUs.Currently, many different remote GPU virtualization frameworks exist, all of them presenting very different characteristics. These differences among them may lead to differences in performance. In this work we present a performance comparison among the only three CUDA remote GPU virtualization frameworks publicly available at no cost. Results show that performance greatly depends on the exact framework used, being the rCUDA virtualization solution the one that stands out among them. Furthermore, rCUDA doubles performance over CUDA for pageable memory copies.
Abstract-The use of GPUs to accelerate general-purpose scientific and engineering applications is mainstream today, but their adoption in current high-performance computing clusters is impaired primarily by acquisition costs and power consumption. Therefore, the benefits of sharing a reduced number of GPUs among all the nodes of a cluster can be remarkable for many applications. This approach, usually referred to as remote GPU virtualization, aims at reducing the number of GPUs present in a cluster, while increasing their utilization rate.The performance of the interconnection network is key to achieving reasonable performance results by means of remote GPU virtualization. To this end, several networking technologies with throughput comparable to that of PCI Express have appeared recently. In this paper we analyze the influence of InfiniBand FDR on the performance of remote GPU virtualization, comparing its impact on a variety of GPU-accelerated applications with other networking technologies, such as InfiniBand QDR and Gigabit Ethernet. Given the severe limitations of freely available remote GPU virtualization solutions, the rCUDA framework is used as the case study for this analysis. Results show that the new FDR interconnect, featuring higher bandwidth than its predecessors, allows the reduction of the overhead of using GPUs remotely, thus making this approach even more appealing.
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