Multi-GPU systems and Unified Virtual Memory for scientific applications: The case of the NAS multi-zone parallel benchmarks

GPU-based computing systems have become a widely accepted solution for the high-performance-computing (HPC) domain. GPUs have shown highly competitive performance-per-watt ratios and can exploit an astonishing level of parallelism. However, exploiting the peak performance of such devices is a challenge, mainly due to the combination of two essential aspects of multi-GPU execution: memory allocation and work distribution. Memory allocation determines the data mapping to GPUs, and therefore conditions all work distribution schemes and communication phases in the application. Unified Virtual Memory simplifies the codification of memory allocations, but its effects on performance depend on how data is used by the devices and how the devices' driver is going to orchestrate the data transfers across the system. In this paper we present a multi-GPU and Unified Virtual Memory (UM) implementation of the NAS Multi-Zone Parallel Benchmarks which alternate communication and computation phases offering opportunities to overlap these phases. We analyse the programmability and performance effects of the introduction of the UM support. Our experience shows that the programming efforts for introducing UM are similar to those of having a memory allocation per GPU. On an evaluation environment composed of 2 x IBM Power9 8335-GTH and 4 x GPU NVIDIA V100 (Volta), our UM-based parallelization outperforms the manual memory allocation versions by 1.10x to 1.85x. However, these improvements are highly sensitive to the information forwarded to the devices' driver describing the most convenient location for specific memory regions. We analyse these improvements in terms of the relationship between the computational and communication phases of the applications. (C) 2021 The Author(s). Published by Elsevier Inc.