An adaptive load balancer for graph analytical applications on GPUs
arXiv preprint arXiv:1911.09135, 2019•arxiv.org
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 …
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 …
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.
arxiv.org
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