Locality-Aware Optimizations for Improving Remote Memory Latency in Multi-GPU Systems

Abstract

With generational gains from transistor scaling, GPUs have been able to accelerate traditional computation-intensive workloads. But with the obsolescence of Moore's Law, single GPU systems are no longer able to satisfy the computational and memory requirements of emerging workloads. To remedy this, prior works have proposed tightly-coupled multi-GPU systems. However, multi-GPU systems are hampered from efficiently utilizing their compute resources due to the Non-Uniform Memory Access (NUMA) bottleneck. In this paper, we propose DualOpt, a lightweight hardware-only solution that reduces the remote memory access latency by delivering optimizations catered to a workload's locality profile. DualOpt uses the spatio-temporal locality of remote memory accesses as a metric to classify workloads as cache insensitive and cache-friendly. Cache insensitive workloads exhibit low spatio-temporal locality, while cache-friendly workloads have ample locality that is not exploited well by the conventional cache subsystem of the GPU. For cache insensitive workloads, DualOpt proposes a fine-granularity transfer of remote data instead of the conventional cache line transfer. These remote data are then coalesced so as to efficiently utilize inter-GPU bandwidth. For cache-friendly workloads, DualOpt adds a remote-only cache that can exploit locality in remote accesses. Finally, a decision engine automatically identifies the class of workload and delivers the corresponding optimization, which improves overall performance by 2.5× on a 4-GPU system, with a small hardware overhead of 0.032%.