  Dense Multi-GPU systems have recently gained a lot of attention in the HPC
arena. Traditionally, MPI runtimes have been primarily designed for clusters
with a large number of nodes. However, with the advent of MPI+CUDA applications
and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important
to address efficient communication schemes for such dense Multi-GPU nodes. This
coupled with new application workloads brought forward by Deep Learning
frameworks like Caffe and Microsoft CNTK pose additional design constraints due
to very large message communication of GPU buffers during the training phase.
In this context, special-purpose libraries like NVIDIA NCCL have been proposed
for GPU-based collective communication on dense GPU systems. In this paper, we
propose a pipelined chain (ring) design for the MPI_Bcast collective operation
along with an enhanced collective tuning framework in MVAPICH2-GDR that enables
efficient intra-/inter-node multi-GPU communication. We present an in-depth
performance landscape for the proposed MPI_Bcast schemes along with a
comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The
proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement,
compared to NCCL-based solutions, for intra- and inter-node broadcast latency,
respectively. In addition, the proposed designs provide up to 7% improvement
over NCCL-based solutions for data parallel training of the VGG network on 128
GPUs using Microsoft CNTK.
