Adaptive executions of hyperbolic block-structured AMR applications on GPU systems. (May 2015)

• Record Type: Journal Article
• Authors: Raghavan, Hari K; Vadhiyar, Sathish S
• Is Part Of: International journal of high performance computing applications. Volume 29:Number 2(2015:Summer)
• Page Start: 135
• Page End: 153
• Publication Date: 2015-05
• Subjects: Adaptive mesh refinement -- GPU executions -- dynamic load balancing -- asynchronous executions of CPUs and GPUs -- coalesced access; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342014545546
• Languages: English
• ISSNs: 1094-3420

Abstract

A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies.