Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

TL;DR

This paper explores multi-GPU performance optimization of a CFD code using OpenACC across various platforms, focusing on domain decomposition, communication strategies, and hardware-specific enhancements to improve scalability and efficiency.

Contribution

The paper introduces a set of performance optimizations, including data packing, variable-based data transfer, and GPUDirect, tailored for multi-GPU CFD simulations with significant scalability improvements.

Findings

Performance improved by a factor of 2 with data packing/unpacking.

Communication overhead reduced by variable-based data transfer.

GPUDirect enhances GPU-to-GPU communication efficiency.

Abstract

This paper investigates the multi-GPU performance of a 3D buoyancy driven cavity solver using MPI and OpenACC directives on different platforms. The paper shows that decomposing the total problem in different dimensions affects the strong scaling performance significantly for the GPU. Without proper performance optimizations, it is shown that 1D domain decomposition scales poorly on multiple GPUs due to the noncontiguous memory access. The performance using whatever decompositions can be benefited from a series of performance optimizations in the paper. Since the buoyancy driven cavity code is latency-bounded on the clusters examined, a series of optimizations both agnostic and tailored to the platforms are designed to reduce the latency cost and improve memory throughput between hosts and devices efficiently. First, the parallel message packing/unpacking strategy developed for noncontiguous data movement between hosts and devices improves the overall performance by about a factor of 2. Second, transferring different data based on the stencil sizes for different variables further reduces the communication overhead. These two optimizations are general enough to be beneficial to stencil computations having ghost changes on all of the clusters tested. Third, GPUDirect is used to improve the communication on clusters which have the hardware and software support for direct communication between GPUs without staging CPU's memory. Finally, overlapping the communication and computations is shown to be not efficient on multi-GPUs if only using MPI or MPI+OpenACC. Although we believe our implementation has revealed enough overlap, the actual running does not utilize the overlap well due to a lack of asynchronous progression.