## 3 Execution Framework  
Jobs and measurements were carried out on ENEA's CRESCO6 cluster [48], a modern HPC facility consisting of 434 physical nodes, each of which has two sockets of 24 cores with 2.10 GHz Intel(R) Xeon(R) Platinum 8160 processors and 192 GB RAM (i.e., a total of 20,832 cores and over 80 TB RAM). An Intel Omni-Path 100 Gb/s network interconnects the machines. More importantly, from the point of view of this work, each CRESCO6's node is also equipped with a batch of sensors capable of making several types of measurements (e.g., power consumption, utilisation rate, temperature; each related to physical nodes, single CPUs, memory banks, cooling fans etc.) and various environmental sensors are placed in the server room to also provide information about the whole datacenter and its cooling system. See [49] for a detailed overview of the sensor infrastructure. CRESCO6 is managed by the LSF scheduler ([[https://www.ibm.com/docs/en/spectrum-lsf/10.10?topic=lsf-session-scheduler](https://www.ibm.com/docs/en/spectrum-lsf/10.10?topic=lsf-session-scheduler)]([https://www.ibm.com/docs/en/spectrum-lsf/10.10?topic=lsf-session-scheduler](https://www.ibm.com/docs/en/spectrum-lsf/10.10?topic=lsf-session-scheduler)) accessed on 29 April 2025) and shared with many users. As shown in Figure 1, the system provides two login nodes on which job runs can be built and submitted. One node is designated as a monitoring node to display and retrieve the data measured by the sensors. To generate a dataset for subsequent analysis, we performed 7200 job runs. Each job is uniquely characterised by the input parameter configurations of the considered solvers: Gaussian-elimination (as implemented in PDGESV and PSGESV routines of ScaLAPACK library [50]) and the inhibition method (IMe) (as parallelly implemented in the C-IMeFT procedure [51]). Both solvers are implemented as fault-tolerant MPI programs, but present key differences. In particular, since the ScaLAPACK routines are not fault-tolerant, this feature is achieved through checkpointing in our implementation. Differently, C-IMeFT employs the algorithm-based fault tolerance (ABFT) [52] and, therefore, it is intrinsically able to guarantee the computation from up to a configurable number of anywhere-located hard faults. See [51] for a detailed description and an evaluation of the two methods. As regards the generation of the dataset, the manual management of the broad spectrum of possible configurations for the considered solvers would have been both impractical and inefficient. To address this challenge, we developed a dedicated framework with the primary objective of fully automating the process of generating and executing the combinatorial explosion of different job configurations. Figure 2 shows a diagram of how the submission and execution of a job take place. In the following, we provide a comprehensive exposition of the framework, underscoring its pivotal role in streamlining and optimising the process of execution and data collection. As shown in Figure 2, the starting point of the designed framework is the _Launch Step_, which is responsible for the automatic generation of job submission commands. It encompasses the configurations for the various dimensions explored and the binding settings necessary to meet the resource requirements. Since the CRESCO6 cluster is used daily by many users, a simple launch of the devised jobs would have produced sensor measures highly influenced by the degree of infrastructure utilisation. To avoid this, the launch step is responsible for ensuring that the following requirements are met:  
1. No other user has to access/use the nodes employed by a job;
2. The LSF job must use the minimum possible number of nodes;
3. Jobs must be executed in sequence. Figure 1: Architecture of CRESCO6 HPC systems. As each node consists of 48 cores, in order to meet the first requirement, the launch step always requests to LSF a number of cores equal to 48 multiplied by the desired number of nodes.