LEO performs cross-vendor backward slicing from stalled GPU instructions to attribute root causes to source code, enabling optimizations that produce geometric-mean speedups of 1.73-1.82x on 21 workloads.
GPUscout: Locating data movement-related bottlenecks on GPUs
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Reinforcement learning with imitation learning and reward shaping improves online workload shifting in a one-turbine one-data-center simulation but remains below an offline optimizer that sees the full day.
citing papers explorer
-
LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backward Slicing
LEO performs cross-vendor backward slicing from stalled GPU instructions to attribute root causes to source code, enabling optimizations that produce geometric-mean speedups of 1.73-1.82x on 21 workloads.
-
Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
Reinforcement learning with imitation learning and reward shaping improves online workload shifting in a one-turbine one-data-center simulation but remains below an offline optimizer that sees the full day.