Scalable Implementation of a MapReduce-based Graph Processing Algorithm for Large-scale Heterogeneous Supercomputers

Fast processing for extremely large-scale graph is becoming increasingly important in various domains such as health care, social networks, intelligence, system biology, and electric power grids. The GIM-V algorithm based on MapReduce programing model is designed as a general graph processing method for supporting petabyte-scale graph data. On the other hand, recent large-scale data-intensive computing systems tend to employ GPU accelerators to gain good peak performance and high memory bandwidth, however, the validity of acceleration, including optimization techniques, of the GIM-V algorithm using GPUs is an open problem. To address the problem, we implemented a multi-GPU-based GIM-V application with load balance optimization between GPU devices. Our implementation extends the existing MapReduce library for supporting multi-GPU-environments using the MPI library and optimizes load balance between GPU devices by employing task scheduling-based graph partitioning. We conducted our implementation on the TSUBAME2.0 supercomputer using 256 nodes (6144 hyper-threaded CPU cores, 768 GPUs). The results exhibit that our GPU-based implementation performed 87.04 ME/s on 2(30) (1.07 billion) vertices and 2(34) (17.2 billion) edges, and 1.52 times faster than the CPU-based naive implementation with 2(29) vertices and 2(33) edges. We also studied the performance characteristics of our implementation and load balance optimization technique.