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 intrinsic instructions. Applied to NVIDIA A100 and H100 GPUs, it reveals NUMA node architecture, SM-NUMA relationships, and NUMA-aware memory allocation strategies for cache coherence. This matters because it is the first detailed dissection of GPU memory subsystem NUMA, enabling better application optimization and architectural design.