Abstract: Information Technology (IT) impacts nearly every aspect of our lives and work, and is an essential tool for research. However, research presents new problems in IT which make its support challenging. These challenges are often unlike those experienced by typical organisational IT departments, and relate to the use of new, less stable or more cutting edge technologies and approaches. Supporting researchers in meeting these challenges has led to the creation of a network of specialist units at Swiss research organisations to provide Research IT support. These provide specialist support, letting researchers concentrate on their core tasks and speeding time to results. They are further federated through the eScience Coordination Team (eSCT, a Swiss national project) into a national network to support research. Here we discuss the difference between Core IT functions, and the research IT functions this new network seeks to support. This differentiation helps both Core IT and Research IT teams better make use of their skills and better serve their customers. Beyond this, we reflect on the organisational experiences generated through creating and operating these units and the national network they contribute to, and what lessons can be learned to assist with creation of Research IT functions elsewhere.

Abstract: Optimization of power networks is a rich research area that focuses mainly on efficiently generating and distributing the right amount of power to meet demand requirements across various geographically dispersed regions. The Unit Commitment (UC) problem is one of the critical problems in power network research that involves determining the amount of power that must be produced by each generator in the power network subject to numerous operational constraints. Growth of these networks coupled with increased interconnectivity and cybersecurity measures has created an encouraging platform for applying decentralized optimization paradigms. In this paper, we develop a novel asynchronous decentralized optimization framework to solve the UC problem. We demonstrate that our asynchronous approach outperforms conventional synchronous approaches, thereby promising greater gains in computational efficiency.

Abstract: This paper presents a software tool to simulate a practical problem in smart grid systems. A feature of the smart grid is a system self-recovery capability in the occurrence of anomalies, such as a recovery of a power distribution network after an occurrence of a fault. When this system has a capacity for self-recovery, it is called self-healing. The intersection among areas as computer science, telecommunication, automation and electrical engineering, has allowed power systems to gain new technologies. However, because it is a multi-area domain, self-recovery simulation tools in smart grids are often highly complex as well as presenting low fidelity by using approximation algorithms. The main contribution of this paper is a simulator with high fidelity and low complexity in terms of programming, usability and semantics. In this simulator, a computational intelligence technique and a derivative method for calculating the power flow were encapsulated. The result is a software tool with high abstraction and easy customization, aimed at a self-healing system for a reconfiguration of an electric power distribution network.

Abstract: Next generation data centers will likely be based on the emerging paradigm of disaggregated function-blocks-as-a-unit departing from the current state of mainboard-as-a-unit. Multiple functional blocks or bricks such as compute, memory and peripheral will be spread through the entire system and interconnected together via one or multiple high speed networks. The amount of memory available will be very large distributed among multiple bricks. This new architecture brings various benefits that are desirable in today’s data centers such as fine-grained technology upgrade cycles, fine-grained resource allocation, and access to a larger amount of memory and accelerators. An analysis of the impact and benefits of memory disaggregation is presented in this paper. One of the biggest challenges when analyzing these architectures is that memory accesses should be modeled correctly in order to obtain accurate results. However, modeling every memory access would generate a high overhead that can make the simulation unfeasible for real data center applications. A model to represent and analyze memory disaggregation has been designed and a statistics-based queuing-based full system simulator was developed to rapidly and accurately analyze applications performance in disaggregated systems. With a mean error of 10%, simulation results pointed out that the network layers may introduce overheads that degrade applications’ performance up to 66%. Initial results also suggest that low memory access bandwidth may degrade up to 20% applications’ performance.

Abstract: The power consumption of the High Performance Computing (HPC) systems is an increasing concern as large-scale systems grow in size and, consequently, consume more energy. In response to this challenge, we have develop and evaluate new energy-aware load balancers to reduce the average power demand and save energy of parallel systems when scientific applications with imbalanced load are executed. Our load balancers combine dynamic load balancing with DVFS techniques in order to reduce the clock frequency of underloaded computing cores which experience some residual imbalance even after tasks are remapped. The results show that our load balancers present power reductions of 7.5% in average with the fine-grained variant that performs per-core DVFS, and of 18.75% with the coarse-grained variant that performs per-chip DVFS over real applications.