Achieving Computation-Communication Overlap with Overdecomposition on GPU Systems

J Choi and DF Richards and LV Kale, PROCEEDINGS OF 2020 IEEE/ACM FIFTH INTERNATIONAL WORKSHOP ON EXTREME SCALE PROGRAMMING MODELS AND MIDDLEWARE (ESPM2 2020), 1-10 (2020).

DOI: 10.1109/ESPM251964.2020.00006

The landscape of high performance computing is shifting towards a collection of multi-GPU nodes, widening the gap between on-node compute and off-node communication capabilities. Consequently, the ability to tolerate communication latencies and maximize utilization of the compute hardware are becoming increasingly important in achieving high performance. Overdecomposition has been successfully adopted on traditional CPU-based systems to achieve computation-communication overlap, significantly reducing the impact of communication on application performance. However, it has been unclear whether overdecomposition can provide the same benefits on modern GPU systems. In this work, we address the challenges in achieving computation- communication overlap with overdecomposition on GPU systems using the Charm++ parallel programming system. By prioritizing communication with CUDA streams in the application and supporting asynchronous progress of GPU operations in the Charm++ runtime system, we obtain improvements in overall performance of up to 50% and 47% with proxy applications Jacobi3D and MiniMD, respectively.

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