Towards GPU-Accelerated Large-Scale Graph Processing in the Cloud

Abstract

Recently, we have witnessed that cloud providers start to offer heterogeneous computing environments. There have been wide interests in both clusters and cloud of adopting graphics processors (GPUs) as accelerators for various applications. On the other hand, large-scale graph processing is important for many data-intensive applications in the cloud. In this paper, we propose to leverage GPUs to accelerate large-scale graph processing in the cloud. Specifically, we develop an in-memory graph processing engine G2 with three non-trivial GPU-specific optimizations. Firstly, we adopt fine-grained APIs to take advantage of the massive thread parallelism of the GPU. Secondly, G2 embraces a graph partition based approach for load balancing on heterogeneous CPU/GPU architectures. Thirdly, a runtime system is developed to perform transparent memory management on the GPU, and to perform scheduling for an improved throughput of concurrent kernel executions from graph tasks. We have conducted experiments on an Amazon EC2 virtual cluster of eight nodes. Our preliminary results demonstrate that 1) GPU is a viable accelerator for cloud-based graph processing, and 2) the proposed optimizations improve the performance of GPU-based graph processing engine. We further present the lessons learnt and open problems towards large-scale graph processing with GPU accelerations.