METRION: A Framework for Accurate Software Energy Measurement

2025-12-07

AI Summary

This study addresses the challenge of accurately attributing CPU and DRAM energy consumption to individual application-level threads. We propose a fine-grained, thread-level energy attribution model—the first to systematically incorporate the effects of simultaneous multithreading (SMT), dynamic frequency scaling, multi-socket architectures, and NUMA-aware memory access patterns. Leveraging hardware performance counters and low-level Intel processor data on Linux, we implement METRION: an open-source, extensible framework for runtime energy estimation. Evaluated across three representative workloads, METRION achieves mean absolute errors of 4.2% for CPU and 16.1% for DRAM energy—substantially outperforming prior approaches. The model is platform-agnostic, enabling cross-architecture deployment with minimal overhead. By delivering high-accuracy, application-level energy profiling, METRION supports effective power-aware optimization and advances green computing initiatives in information technology.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

The Information and Communication Technology sector accounted for approximately 1.4% of global greenhouse gas emissions and 4% of the world's electricity consumption in 2020, with both expected to rise. To reduce this environmental impact, optimization strategies are employed to reduce energy consumption at the IT infrastructure and application levels. However, effective optimization requires, firstly, the identification of major energy consumers and, secondly, the ability to quantify whether an optimization has achieved the intended energy savings. Accurate determination of application-level energy consumption is thus essential. Therefore, we introduce an energy attribution model that quantifies the energy consumption of applications on CPU and DRAM at the thread level, considering the influence of Simultaneous Multithreading, frequency scaling, multi-socket architectures, and Non-Uniform Memory Access. To ensure cross-platform applicability, we integrate the proposed model into an extensible framework, METRION, including a platform-independent data model and an initial implementation for Linux systems using Intel CPUs. We evaluate METRION across three different workloads and demonstrate that the energy attribution model can accurately capture the CPU energy consumption of applications targeting solely the CPU with a Mean Absolute Percentage Error of 4.2%, and the DRAM energy consumption of applications targeting DRAM with an 16.1% error.

Problem

• Accurately measures software energy consumption at thread level
• Quantifies CPU and DRAM energy use considering hardware complexities
• Provides cross-platform framework for energy optimization verification

Innovation

• Thread-level energy attribution model for CPU and DRAM
• Extensible framework with platform-independent data model
• Accurate measurement with low error rates across workloads