Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters
🤖 AI Summary
High energy consumption and associated carbon emissions from large-scale scientific workflows in HPC clusters pose a critical sustainability challenge. This paper presents the first cross-disciplinary quantification of carbon footprints across three real-world scientific workflows. We propose an end-to-end energy-efficiency optimization paradigm integrating heterogeneous architecture adaptation, compiler-level energy-aware code optimization, DVFS (Dynamic Voltage and Frequency Scaling), node-level load consolidation, and energy-aware workflow scheduling. Moving beyond single-dimensional optimization, our approach enables holistic hardware–software co-optimization for carbon reduction. Evaluated on representative scientific workflows, it achieves 15–40% reductions in both energy consumption and carbon emissions across the full execution pipeline, significantly improving energy efficiency. The work delivers a reproducible, deployable technical framework and empirically validated benchmarks for green high-performance computing.
Application Category
📝 Abstract
Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these workflow applications can have a considerable environmental footprint in terms of compute resource use, energy consumption, and carbon emissions. Mitigating this is critical in light of climate change and the urgent need to reduce carbon emissions. In this chapter, we exemplify the problem by estimating the carbon footprint of three real-world scientific workflows from different scientific domains. We then describe techniques for reducing the energy consumption and, thereby, carbon footprint of individual workflow tasks and entire workflow applications, such as using energy-efficient heterogeneous architectures, generating optimised code, scaling processor voltages and frequencies, consolidating workloads on shared cluster nodes, and scheduling workloads for optimised energy efficiency.
Problem
Research questions and friction points this paper is trying to address.
Reducing energy consumption in scientific compute clusters
Minimizing carbon emissions from large-scale workflow applications
Optimizing workflow tasks for energy-efficient execution
Innovation
Methods, ideas, or system contributions that make the work stand out.
Energy-efficient heterogeneous architectures for workflows
Processor voltage and frequency scaling techniques
Workload consolidation and energy-aware scheduling
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