In recent decades, driven by global efforts towards sustainability, the priorities of HPC facilities have changed to include maximising energy efficiency besides computing performance. In this regard, a crucial open question is how to accurately predict the contribution of each parallel job to the system’s energy consumption. Accurate estimations in this sense could offer an initial insight into the overall power requirements of the system, and provide meaningful information for, e.g., power-aware scheduling, load balancing, infrastructure design, etc. While ML-based attempts employing large training datasets of past executions may suffer from the high variability of HPC workloads, a more specific knowledge of the nature of the jobs can improve prediction accuracy. In this work, we restrict our attention to the rather pervasive task of linear system resolution. We propose a methodology to build a large dataset of runs (including the measurements coming from physical sensors deployed on a large HPC cluster), and we report a statistical analysis and preliminary evaluation of the efficacy of the obtained dataset when employed to train well-established ML methods aiming to predict the energy footprint of specific software.

The scale of nowadays High Performance Computing (HPC) systems is the key element that determines the achievement of impressive performance, as well as the reason for their relatively limited reliability. Over the last decade, specific areas of the High Performance Computing (HPC) research field have addressed the issue at different levels, by enriching the infrastructure, the platforms, or the algorithms with fault tolerance features. In this work, we focus on the rather-pervasive task of computing the solution of a dense, unstructured linear system and we propose an algorithm-based technique to obtain fault tolerance to multiple anywhere-located faults during the parallel computation. We particularly study the ways to boost the performance of the rollback-free recovery, and we provide an extensive evaluation of our technique w.r.t. to other state-of-the-art algorithm-based methods.

High-Performance Computing (HPC) systems today are gradually increasing in size and complexity due to the correspondent demand for ever-increasing computing needs, requiring more complicated tasks and higher accuracy. The growing energy needs of HPC systems require the urgent adoption of green HPC approaches to mitigate environmental impact and promote energy-efficient computing. This paper proposes a monitoring solution for the energy consumption during the execution of parallel software focusing in particular on the solution of linear systems in HPC systems: the Inhibition Method and Gaussian Elimination from ScaLAPACK library. The main goal is to profile their execution from the energy consumption perspective. The approach follows a white-box paradigm, injecting the monitoring component into specific ranks. Despite a slight overhead compromise due to synchronization, this design permits accurate measurements.