## 1 Introduction  
The computing capabilities of high-performance computing (HPC) systems have impressively increased in recent decades. This remarkable growth inevitably comes with a dramatic increment in power consumption [1]. Therefore, just like sustainability is becoming a key challenge in many human activities, maximising energy efficiency--besides performance--is becoming a priority for HPC facilities too. Several works [2] focus on hardware efficiency by proposing technologies to reduce the environmental footprints of microprocessors, chips, and devices. Some recent attempts [3, 4, 5, 6] focus instead on the software level by studying predictive models to assess the power required by parallel algorithms. The latter set of works stems from the observation that each execution of a job on an HPC infrastructure requires the allocation of specific resources, thus contributing to the system's total energy consumption. The ambitious goal is therefore to predict the energy consumption of an HPC system based on the energy consumed by individual jobs: a feature that could be particularly useful for many HPC tasks (e.g., energy-aware workload management, load balancing, infrastructure design, etc.). For example, if the HPC scheduler could rely on accurate prediction of the job's energy profilesover time, it could take into account this information to allocate the jobs on the physical machines in a way that reduces the energy consumed by the whole infrastructure. More futuristics applications include energy consumption optimisations at compile time and programming tools able to suggest more energy saving ways to write the same algorithm (just as some developing tools are already able to understand the intent of the programmer as they write the code and suggest more computationally efficient solutions). Software-level solutions can be divided into two categories: some works propose theoretical energy models [7; 8; 9; 10] to mathematically connect the algorithm's steps with its energy footprint, whereas other techniques [11; 12; 13; 14; 15; 16; 17] try to predict the job's footprint based on historical records, usually by employing machine learning (ML) techniques. In this regard, a necessary condition to obtain accurate models is the availability of large sets of examples of past jobs' executions. However, the high variability in the characteristics of the workloads submitted to an HPC infrastructure (e.g., the kind of the algorithm, its CPU and RAM requirements, number of accesses to disk, inter-process communication scheme, etc.) can hinder the accuracy of the predictions, even if numerous historical examples are made available [12; 17; 18]. In general, fine-grained knowledge about the nature of the jobs can be crucial to improve the prediction accuracy of ML techniques. In this work, we propose a first step towards this direction by restricting the attention to a particular kind of HPC workload, the parallel resolution of large-scale linear systems, and providing the scientific community with an extensive dataset of job runs and corresponding measured performance. The choice to focus on linear solvers stems from the observation that linear systems are widely used in the scientific field and often constitute the most suitable mathematical model to represent human activities, especially in technological and industrial contexts. Furthermore, as the complexity (and duration) of the resolution process is strictly connected to the matrix rank, the workload generated when solving large systems can have a significant impact on the power consumption of the whole application. Our analysis relies on highly accurate measurements of energy performance, obtained through the use of sensors directly installed on each node of an HPC cluster, thus overcoming the limitations [19; 20] of on-chip energy monitoring frameworks such as Intel's running average power limit (RAPL) [21]. The proposed dataset is particularly suitable for training ML techniques aiming to predict the energy consumption of HPC jobs.