Abstract
Complex applications in distributed-memory parallel systems often follow a demand-driven approach with domain decomposition. A uniform data distribution among the local memories at the processing elements may require frequent remote data access. To keep the processors busy while data is remotely fetched, concurrent application processes are assigned to each transputer-based processing element. Adding more concurrent application processes in a large-scale parallel system may degrade performance, due to the traffic increase with data requests and data block replies. A conditional broadcast mechanism is implemented during data requests, to limit this flow of messages. Monitoring strategies are proposed to further reduce the messages density, and a parameterized model to measure and evaluate global execution times is presented. Simulation data running the model with up to 35 transputers show that monitoring can reduce the performance degradation when more local concurrence is added. However, if too much data replication is present, the simulation data also show that the supply of communication services at each node still imposes a burden, requiring complementary monitoring strategies to allow removal of redundant reply messages.

Abstract
Many of the current approaches used in teaching and learning in engineering education are not the most appropriate to prepare students for the challenges they will face in their professional careers. The active involvement of students in their learning process facilitates the development of the technical and professional competencies they need as professionals. This article describes the organisation and impact of a mini-conference and project work - the creation of a software product and its introduction in the market - aimed at the development of professional competencies in general and writing skills in particular. The course was evaluated by assessing the students' perception of the development of a number of professional competencies through a questionnaire completed by 125 students from two consecutive year groups. The results indicate that the project work and the mini-conference had a positive impact on students' perceptions of the development of professional competencies. © 2012 Copyright SEFI.

Abstract
Load distribution is a well known critical problem in every distributed system. From operating systems to agent oriented applications it is not difficult to find cases where processing nodes are overloaded when, at the same time, other peers present low levels of activity. In agent oriented applications, where the appeal to cooperation is almost a constant event, these unbalanced situations may generate serious cases of contention, deadlock or simply large idle times. The implementation of load distribution strategies in a distributed system may help significantly to improve its overall performance and reduce effectively such undesirable situations. In order to study the effects of different load distribution policies in agent based applications a generic load distribution simulation system was design and implemented. The system allows the specification of multiorganisational distributed systems with dynamic load patterns. Its main characteristics and functionalities are presented in this paper.

Abstract
Exploiting the computing power of the diversity of resources available on heterogeneous systems is mandatory but a very challenging task. The diversity of architectures, execution models and programming tools, together with disjoint address spaces and different computing capabilities, raise a number of challenges that severely impact on application performance and programming productivity. This problem is further compounded in the presence of data parallel irregular applications. This paper presents a framework that addresses development and execution of data parallel irregular applications in heterogeneous systems. A unified task-based programming and execution model is proposed, together with inter and intra-device scheduling, which, coupled with a data management system, aim to achieve performance scalability across multiple devices, while maintaining high programming productivity. Intra-device scheduling on wide SIMD/SIMT architectures resorts to consumer-producer kernels, which, by allowing dynamic generation and rescheduling of new work units, enable balancing irregular workloads and increase resource utilization. Results show that regular and irregular applications scale well with the number of devices, while requiring minimal programming effort. Consumer-producer kernels are able to sustain significant performance gains as long as the workload per basic work unit is enough to compensate overheads associated with intra-device scheduling. This not being the case, consumer kernels can still be used for the irregular application. Comparisons with an alternative framework, StarPU, which targets regular workloads, consistently demonstrate significant speedups. This is, to the best of our knowledge, the first published integrated approach that successfully handles irregular workloads over heterogeneous systems.

Abstract
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, is less clear. Through a detailed analysis of L & ouml;wner inequalities between quantum and classical FIMs, this study uncovers the nuanced distinctions and implications of using each type of FIM. Our results indicate that a PQC-based agent using the quantum FIM without additional insights typically incurs a larger approximation error and does not guarantee improved performance compared to the classical FIM. Empirical evaluations in classic control benchmarks suggest even though quantum FIM preconditioning outperforms standard gradient ascent, in general, it is not superior to classical FIM preconditioning.

Abstract
Reinforcement learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networks (NNs), RL is capable of solving extremely complex problems. Deep Q-Learning, a RL algorithm that uses Deep NNs, has been shown to achieve super-human performance in game-related tasks. Nonetheless, it is also possible to use Variational Quantum Circuits (VQCs) as function approximators in RL algorithms. This work empirically studies the performance and trainability of such VQC-based Deep Q-Learning models in classic control benchmark environments. More specifically, we research how data re-uploading affects both these metrics. We show that the magnitude and the variance of the model's gradients remain substantial throughout training even as the number of qubits increases. In fact, both increase considerably in the training's early stages, when the agent needs to learn the most. They decrease later in the training, when the agent should have done most of the learning and started converging to a policy. Thus, even if the probability of being initialized in a Barren Plateau increases exponentially with system size for Hardware-Efficient ansatzes, these results indicate that the VQC-based Deep Q-Learning models may still be able to find large gradients throughout training, allowing for learning.