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.
Coordinated Power Management on Heterogeneous Systems
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abstract
Performance prediction is essential for energy-efficient computing in heterogeneous computing systems that integrate CPUs and GPUs. However, traditional performance modeling methods often rely on exhaustive offline profiling, which becomes impractical due to the large setting space and the high cost of profiling large-scale applications. In this paper, we present OPEN, a framework consists of offline and online phases. The offline phase involves building a performance predictor and constructing an initial dense matrix. In the online phase, OPEN performs lightweight online profiling, and leverages the performance predictor with collaborative filtering to make performance prediction. We evaluate OPEN on multiple heterogeneous systems, including those equipped with A100 and A30 GPUs. Results show that OPEN achieves prediction accuracy up to 98.29\%. This demonstrates that OPEN effectively reduces profiling cost while maintaining high accuracy, making it practical for power-aware performance modeling in modern HPC environments. Overall, OPEN provides a lightweight solution for performance prediction under power constraints, enabling better runtime decisions in power-aware computing environments.
fields
cs.DC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EcoShift uses online performance prediction plus dynamic programming to reallocate reclaimed power in heterogeneous CPU-GPU clusters, delivering up to 6% average performance gain while staying inside the total power limit.
citing papers explorer
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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.
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EcoShift: Performance-Aware Power Management for Power-Constrained Heterogeneous Systems
EcoShift uses online performance prediction plus dynamic programming to reallocate reclaimed power in heterogeneous CPU-GPU clusters, delivering up to 6% average performance gain while staying inside the total power limit.