EcoSched jointly selects GPU counts via lightweight profiling and coschedules jobs with a score-based policy plus NUMA placement, delivering up to 14.8% energy savings, 30.1% makespan reduction, and 40.4% EDP improvement on H100/A100/V100 systems.
Accelerated training for cnn distributed deep learning through automatic resource-aware layer placement
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Towards Energy Efficient Co-Scheduling in HPC
EcoSched jointly selects GPU counts via lightweight profiling and coschedules jobs with a score-based policy plus NUMA placement, delivering up to 14.8% energy savings, 30.1% makespan reduction, and 40.4% EDP improvement on H100/A100/V100 systems.