DGNA: Dissecting GPU NUMA Architecture through Microbenchmarking and Data Analysis

The problem is that modern GPU memory architectures, particularly NUMA mechanisms within L2 and DRAM, remain black-boxes, hindering optimization and simulation. DGNA introduces a methodology using microbenchmarking and Gaussian mixture models to measure L2 and DRAM latency without relying on intrinsic instructions. Applied to NVIDIA A100 and H100 GPUs, it reveals NUMA node architecture, SM-NUMA relationships, and coherence-aware allocation strategies. This matters because it is the first work to detail GPU memory subsystem NUMA, enabling better application tuning and architectural design.