S. Iserte

P. Rosciszewski, A. Krzywaniak, S. Iserte et al.

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of \emph{AI for simulation}, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a \emph{computational fluid dynamics} application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the \emph{popdist} library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34\% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s.

S. Catalán, R. Carratalá-Sáez, S. Iserte

High Performance Computing (HPC) is a highly demanded discipline in companies and institutions. However, as students and also afterwards as professors, we observed a lack of HPC related content in the engineering degrees at our university, including Computer Science. Thus, we designed and offered the engineering students a non-mandatory course entitled ``Build you own Raspberry Pi cluster employing Raspberry Pi'' to provide the students with HPC skills. With this course, we covered the basics of supercomputing (hardware, networking, software tools, performance evaluation, cluster management, etc.). This was possible thanks to leveraging the flexibility and versatility of Raspberry Pi devices, and the students' motivation that arose from the hands-on experience. Moreover, the course included a ``Teaching on demand'' component to let the attendees choose a field to explore, based on their own interests. In this paper, we offer all the details to let anyone fully reproduce the course. Besides, we analyze and evaluate the methodology that let us fulfill our objectives: increase the students' HPC skills and knowledge in such a way that they feel capable of utilizing it in their mid-term professional career.

S. Iserte, P. Carratalà, R. Arnau et al.

The pressure for Water Resource Recovery Facilities (WRRF) operators to efficiently treat wastewater is greater than ever because of the water crisis, produced by the climate change effects and more restrictive regulations. Technicians and researchers need to evaluate WRRF performance to ensure maximum efficiency. For this purpose, numerical techniques, such as CFD, have been widely applied to the wastewater sector to model biological reactors and secondary settling tanks with high spatial and temporal accuracy. However, limitations such as complexity and learning curve, prevent extending CFD usage among wastewater modeling experts. This paper presents HydroSludge, a framework that provides a series of tools that simplify the implementation of the processes and workflows in a WRRF. This work leverages HydroSludge to preprocess existing data, aid the meshing process, and perform CFD simulations. Its intuitive interface proves itself as an effective tool to increase the efficiency of wastewater treatment

S. Iserte, M. Madon, G. Da et al.

Dynamic Resource Management (DRM) techniques can be leveraged to maximize throughput and resource utilization in computational clusters. Although DRM has been extensively studied through analytical workloads and simulations, skepticism persists among end administrators and users regarding their feasibility under real-world conditions. To address this problem, we propose a novel methodology for validating DRM techniques, such as malleability, in realistic scenarios that reproduce actual cluster conditions of jobs and users by replaying workload logs on a High-performance Computing (HPC) infrastructure. Our methodology is capable of adapting the workload to the target cluster. We evaluate our methodology in a malleability-enabled 125-node partition of the Marenostrum 5 supercomputer. Our results validate the proposed method and assess the benefits of MPI malleability on a novel use case of a pioneer user of malleability (our "PhD Student"): parallel efficiency-aware malleability reduced a malleable workload time by 27% without delaying the baseline workload, although introducing queueing delays for individual jobs, but maintaining the resource utilization rate.

S. Iserte, V. R. Tomas, M. Pérez et al.

Team project-based learning (TPBL) combines two learning techniques: project-based learning (PBL) and teamwork. This combination leverages the learning outcomes of both methods and places students in a real work situation where they must develop and solve a real project while working as a team. TPBL has been used in two advanced database subjects in Jaume I University (UJI)'s Computer Science degree program. This learning method was used for four years (academic years from 2018/19 to 2021/2022) with positive outcomes. This study presents the project development, which includes teamwork formation, activities, timetable, and exercised learning competencies (both soft and specific). Further, the project's results were evaluated from three different perspectives: a) teamwork evaluation by teammates, b) Students' opinions on the subject and project, and c) subject final grades.