3 citations · 6 across the 16 of their papers we have counts for
4 papers · 1 filter
Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency
Marco Kurzynski, Shaizeen Aga, Di Wu
Training large language models (LLMs) efficiently requires a deep understanding of how modern GPU systems behave under real-world distributed training workloads. While prior work h…
Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap
Shagnik Pal, Shaizeen Aga, Suchita Pati +2
Modern ML workloads demand distributing training and inference across multiple GPUs. However, these parallelization techniques often suffer from exposed critical-path communication…
Lit Silicon: A Case Where Thermal Imbalance Couples Concurrent Execution in Multiple GPUs
Marco Kurzynski, Shaizeen Aga, Di Wu
GPU systems are increasingly powering modern datacenters at scale. Despite being highly performant, GPU systems can exhibit performance variation at the node and cluster levels. Su…
DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication
Suchita Pati, Shaizeen Aga, Mahzabeen Islam +3
Offloading communication to existing direct memory access (DMA) engines, available on most state-of-the-art commercial GPUs, has emerged as a practical and low-cost solution to eff…