Calibrating DRAMPower Model for HPC: A Runtime Perspective from Real-Time Measurements

TL;DR

This paper introduces a runtime calibration method for the DRAMPower model using real-system measurements, significantly improving its accuracy for energy estimation in HPC systems.

Contribution

It presents a novel calibration approach that refines DRAMPower parameters based on actual energy measurements, addressing limitations of existing models.

Findings

Calibration reduces energy estimation error to below 5%.

Refined model better captures system-level factors affecting power.

Improves reliability of DRAMPower for energy-aware optimization.

Abstract

Main memory's rising energy consumption has emerged as a critical challenge in modern computing architectures, particularly in large-scale systems, driven by frequent access patterns, growing data volumes, and insufficient power management strategies. Accurate modeling of DRAM power consumption is essential to address this challenge and optimize energy efficiency. However, existing modeling tools often rely on vendor-provided datasheet values that are obtained under worst-case or idealized conditions. As a result, they fail to capture important system-level factors, such as temperature variations, chip aging, and workload-induced variability, which leads to significant discrepancies between estimated and actual power consumption observed in real deployments. In this work, we propose a runtime calibration methodology for the DRAMPower model using energy measurements collected from real-system experiments. By applying custom memory benchmarks on an HPC cluster and leveraging fine-grained power monitoring infrastructure, we refine key current parameters (IDD values) in the model. Our calibration reduces the average energy estimation error to less than 5%, substantially improving modeling accuracy and making DRAMPower a more reliable tool for power-aware system design and optimization on the target server platform.