Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms Using System-Level Measurements

Abstract

Modern high-performance computing platforms, cloud computing systems, and data centers are highly heterogeneous containing nodes where a multicore CPU is tightly integrated with accelerators. An important challenge for energy optimization of hybrid parallel applications on such platforms is how to accurately estimate the energy consumption of application components running on different compute devices of the platform. In this work, we propose a method for accurate estimation of the application component-level energy consumption employing system-level power measurements with power meters. We experimentally validate the method on a cluster of two hybrid heterogeneous computing nodes using three parallel applications -matrix-matrix multiplication, 2D fast Fourier transform and gene sequencing. The experiments demonstrate a high estimation accuracy of the proposed method, with the average estimation error ranging between 2% and 5%. The average error demonstrated by the state-of-the-art estimation methods for the same experimental setup ranges from 15% to 75%, while the maximum reaches 178%. We also show that the use of the state-of-the-art estimation methods instead of the proposed one in the energy optimization loop leads to significant energy losses (up to 45% in our case). INDEX TERMS Energy modelling, energy optimization, power meters, on-chip power sensors, heterogeneous platforms, parallel applications, multicore CPU, GPU, Intel Xeon Phi, HPC.

A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs

“…It has been used for providing the measurements at a system-level. Fahad et al [25] presented the first methodology (that is, AnMoHA) to measure the component-level energy consumption of a platform using external power meters. The authors demonstrate that their approach provides accurate energy consumption decomposition up to socket-level.…”

A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

“…At the same time, the state-of-the-art statistical approaches consider energy profiles as linear or smooth functions of workload sizes to find their goodness. The failure of capturing the qualitative differences in energy profiles can drastically affect the energy optimization efforts and can cause significant energy losses [13,14].…”

“…Here, the ground-truth refers to the baseline profile or the reference value for the comparison. State-of-the-art but inaccurate energy measurements used in energy optimization of applications can result in significant energy losses [13], up to 84% in some real-life settings [14].…”

“…Pearson correlation coefficient [15] and average prediction error (also known as relative error) [16] are the most commonly used statistical measurements to determine the accuracy of energy profiles. A plethora of research work including [5,13,14,[17][18][19] use average, maximum and minimum prediction errors to determine the accuracy of energy profiles. References [20][21][22][23] are some of the notable works which used the correlation coefficient to determine to determine whether the energy profiles follow the ground truth.…”

“…For example, Rico-Gallego et al [24] argue that the relative error is lower for a profile that underestimates than for a profile that overestimates, and thus can negatively impact the interpretation of the results. Similarly, Fahad et al [13] demonstrate that the two statistical measures do not capture the holistic picture of the energy consumption trend of the profiles, and thus are blind to the qualitative differences of the energy profiles and the ground truth.…”