HPC and energy efficiency using V-nets
Abstract
In today’s era of exascale machines, energy efficiency is more crucial than ever. This study explores the potential of V-nets, initially tested on small-scale machines, to be scaled up for larger systems that support parallelism. By capturing real-time data as sequences of discrete events, this project investigates how V-nets can effectively analyze these event sequences to diagnose system behavior in High-Performance Computing (HPC) systems. The focus is on constructing temporal patterns to assess the energy performance of scalable computing systems. While no specific system is tested, the analysis emphasizes the significance of this innovative formalism. It showcases V-nets ability to identify simultaneous event occurrences, detect partial sequences, and mitigate false positives. This research aims to bridge the gap between theoretical analysis and practical implementation in Industry 4.0, ultimately advancing the optimization of scalable computing systems.
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