3 papers
cs.AR2026
Fleet: Hierarchical Task-based Abstraction for Megakernels on Multi-Die GPUs
Sangeeta Chowdhary, Ryan Swann, Sean Siddens +7
Modern GPUs adopt chiplet-based designs with multiple private cache hierarchies, but current programming models (CUDA/HIP) expose a flat execution hierarchy that cannot express chi…
cs.DC2025
Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs
Octavian Alexandru Trifan, Karthik Sangaiah, Muhammad Awad +5
As large language models (LLMs) continue to scale, their workloads increasingly rely on distributed execution across multiple GPUs. However, the conventional bulk synchronous paral…
cs.AR2025
Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects
Mansi Choudhary, Karthik Sangaiah, Sonali Singh +3
The rise of disaggregated AI GPUs has exposed a critical bottleneck in large-scale attention workloads: non-uniform memory access (NUMA). As multi-chiplet designs become the norm f…