Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained Transfers

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Despite continuing research into inter-GPU communication mechanisms, extracting performance from multi-GPU systems remains a significant challenge. Inter-GPU communication via bulk DMA-based transfers exposes data transfer latency on the GPU’s critical execution path because these large transfers are logically interleaved between compute kernels. Conversely, fine-grained peer-to-peer memory accesses during kernel execution lead to memory stalls that can exceed the GPUs’ ability to cover these operations via multi-threading. Worse yet, these sub-cacheline transfers are highly inefficient on current inter-GPU interconnects. To remedy these issues, we propose PROACT, a system enabling remote memory transfers with the programmability and pipeline advantages of peer-to-peer stores, while achieving interconnect efficiency that rivals bulk DMA transfers. Combining compile-time instrumentation with fine-grain tracking of data block readiness within each GPU, PROACT enables interconnect-friendly data transfers while hiding the transfer latency via pipelining during kernel execution. This work describes both hardware and software implementations of PROACT and demonstrates the effectiveness of a PROACT software prototype on three generations of GPU hardware and interconnects. Achieving near-ideal interconnect efficiency, PROACT realizes a mean speedup of 3.0x over single-GPU performance for 4-GPU systems, capturing 83% of available performance opportunity. On a 16-GPU NVIDIA DGX-2 system, we demonstrate an 11.0x average strong-scaling speedup over single-GPU performance, 5.3x better than a bulk DMA-based approach.


Jeffrey Fessler (University of Michigan)
Thomas Wenisch (University of Michigan)

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