Network Assisted Non-Contiguous Transfers for GPU-Aware MPI Libraries

The importance of GPUs in accelerating HPC applications is evident by the fact that a large number of super-computing clusters are GPU-enabled. Many of these HPC applications use MPI as their programming model. These MPI applications oftentimes exchange data that is non-contiguous in GPU memory. MPI provides Derived Datatypes(DDTs) to represent such data. In the past, researchers have proposed solutions to optimize these MPI DDT based inter-node GPU exchanges. All of these solutions are aimed at optimizing the overheads associated with pack-unpack kernels that facilitate the non-contiguous exchanges. Modern HCAs are capable of gathering/scattering data from/to non-contiguous GPU memory regions. In this work, we analyze the challenges in using HCA's scatter/gather mechanism for GPU-based HPC workloads. We propose a low-overhead HCA-assisted scheme to improve the performance of GPU-based non-contiguous exchanges. We show that the proposed scheme provides up to 2X benefits compared to existing pack-based schemes at the benchmark level. Fur-thermore, on the layouts used by MILC, NASMG, Specfem3D applications, we show that the proposed scheme outperforms the state-of-the MPI libraries such as MVAPICH2-GDR and OpenMPI+UCX.

I. Introduction

Graphics Processing Units (GPUs) have become ubiquitous in modern supercomputers due to their high compute capa-bility and power efficiency. This is particularly evident in the growing number of top supercomputers on the Top500 [17] list deploying GPUs on their clusters.