2019/11/20 by Jatala, Vishwesh, Hoang, Loc, Dathathri, Roshan +3
#Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.1911.09135
Load-balancing among the threads of a GPU for graph analytics workloads is difficult because of the irregular nature of graph applications and the high variability in vertex degrees, particularly in power-law graphs. We describe a novel load balancing scheme to address this problem. Our scheme is implemented in the IrGL compiler to allow users to generate efficient load balanced code for a GPU from high-level sequential programs. We evaluated several graph analytics applications on up to 16 distributed GPUs using IrGL to compile the code and the Gluon substrate for inter-GPU communication. Our experiments show that this scheme can achieve an average speed-up of 2.2x on inputs that suffer from severe load imbalance problems when previous state-of-the-art load-balancing schemes are used.