HiDVFS: A Hierarchical Multi-Agent DVFS Scheduler for OpenMP DAG Workloads
Mohammad Pivezhandi, Abusayeed Saifullah, Ali Jannesari

TL;DR
HiDVFS is a hierarchical multi-agent scheduler designed for OpenMP DAG workloads that optimizes task allocation and core frequencies to improve performance and reduce energy and thermal issues in multicore embedded systems.
Contribution
It introduces a novel hierarchical multi-agent DVFS scheduling approach that considers profiling, temperature, and task priorities for better energy and thermal management.
Findings
Achieves 3.95x speedup on average across benchmarks.
Reduces energy consumption by 47.1% compared to state-of-the-art.
Outperforms GearDVFS with a 3.44x speedup and 50.4% energy savings.
Abstract
With advancements in multicore embedded systems, leakage power, exponentially tied to chip temperature, has surpassed dynamic power consumption. Energy-aware solutions use dynamic voltage and frequency scaling (DVFS) to mitigate overheating in performance-intensive scenarios, while software approaches allocate high-utilization tasks across core configurations in parallel systems to reduce power. However, existing heuristics lack per-core frequency monitoring, failing to address overheating from uneven core activity, and task assignments without detailed profiling overlook irregular execution patterns. We target OpenMP DAG workloads. Because makespan, energy, and thermal goals often conflict within a single benchmark, this work prioritizes performance (makespan) while reporting energy and thermal as secondary outcomes. To overcome these issues, we propose HiDVFS (a hierarchical…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Cloud Computing and Resource Management · Big Data and Digital Economy
