Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard’s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.

A multi-objective genetic algorithm optimisation using variable speed pumps in water distribution systems

Due to its practical importance and inherent complexity, the optimisation of distribution networks for supplying drinking water has been the subject of extensive study for the past 30 years. The optimization is governed by sizing the pipes in the water distribution network (WDN) and / or optimises specific parts of the network such as pumps, tanks etc. or try to analyse and optimise the reliability of a WDN. In this thesis, the author has analysed two different WDNs (Anytown City and Cabrera city networks), trying to solve and optimise a multi-objective optimisation problem (MOOP). The main two objectives in both cases were the minimisation of Energy Cost (€) or Energy consumption (kWh), along with the total Number of pump switches (TNps) during a day. For this purpose, a decision support system generator for Multi-objective optimisation used. Its name is GANetXL and has been developed by the Center of Water System in the University of Exeter. GANetXL, works by calling the EPANET hydraulic solver, each time a hydraulic analysis has been fulfilled. The main algorithm used, was a second-generation algorithm for multi-objective optimisation called NSGA_II that gave us the Pareto fronts of each configuration. The first experiment that has been carried out was the network of Anytown city. It is a big network with a pump station of four fixed speed parallel pumps that are boosting the water dynamics. The main intervention was to change these pumps to new Variable speed driven pumps (VSDPs), by installing inverters capable to diverse their velocity during the day. Hence, it’s been achieved great Energy and cost savings along with minimisation in the number of pump switches. The results of the research are thoroughly illustrated in chapter 7, with comments and a variety of graphs and different configurations. The second experiment was about the network of Cabrera city. The smaller WDN had a unique FS pump in the system. The problem was the same as far as the optimisation process was concerned, thus, the minimisation of the energy consumption and in parallel the minimisation of TNps. The same optimisation tool has been used (GANetXL).The main scope was to carry out several and different experiments regarding a vast variety of configurations, using different pump (but this time keeping the FS mode), different tank levels, different pipe diameters and different emitters coefficient. All these different modes came up with a large number of results that were compared in the chapter 8. Concluding, it should be said that the optimisation of WDNs is a very interested field that has a vast space of options to deal with. This includes a large number of algorithms to choose from, different techniques and configurations to be made and different support system generators. The researcher has to be ready to “roam” between these choices, till a satisfactory result will convince him/her that has reached a good optimisation point.

Design of an innovative electric vehicle simulator for Charging Systems' End-of-Line Testing

The trend related to the turnover of internal combustion engine vehicles with EVs goes by the name of electrification. The push electrification experienced in the last decade is linked to the still ongoing evolution in power electronics technology for charging systems. This is the reason why an evolution in testing strategies and testing equipment is crucial too. The project this dissertation is based on concerns the investigation of a new EV simulator design. that optimizes the structure of the testing equipment used by the company who commissioned this work. Project requirements can be summarized in the following two points: space occupation reduction and parallel charging implementation. Some components were completely redesigned, and others were substituted with equivalent ones that could perform the same tasks. In this way it was possible to reduce the space occupation of the simulator, as well as to increase the efficiency of the testing device. Moreover, the possibility of conjugating different charging simulations could be investigated by parallelly launching two testing procedures on a unique machine, properly predisposed for supporting the two charging protocols used. On the back of the results achieved in the body of this dissertation, a new design for the EV simulator was proposed. In this way, space reduction was obtained, and space occupation efficiency was improved with the proposed new design. The testing device thus resulted to be way more compact, enabling to gain in safety and productivity, along with a 25% cost reduction. Furthermore, parallel charging was implemented in the proposed new design since the conducted tests clearly showed the feasibility of parallel charging sessions. The results presented in this work can thus be implemented to build the first prototype of the new EV simulator.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Feedback control of underactuated cable-driven parallel robots

In this work an Underactuated Cable-Driven Parallel Robot (UACDPR) that operates in the three dimensional Euclidean space is considered. The End-Effector has 6 degrees of freedom and is actuated by 4 cables, therefore from a mechanical point of view the robot is defined underconstrained. However, considering only three controlled pose variables, the degree of redundancy for the control theory can be considered one. The aim of this thesis is to design a feedback controller for a point-to-point motion that satisfies the transient requirements, and is capable of reducing oscillations that derive from the reduced number of constraints. A force control is chosen for the positioning of the End-Effector, and error with respect to the reference is computed through data measure of several sensors (load cells, encoders and inclinometers) such as cable lengths, tension and orientation of the platform. In order to express the relation between pose and cable tension, the inverse model is derived from the kinematic and dynamic model of the parallel robot. The intrinsic non-linear nature of UACDPRs systems introduces an additional level of complexity in the development of the controller, as a result the control law is composed by a partial feedback linearization, and damping injection to reduce orientation instability. The fourth cable allows to satisfy a further tension distribution constraint, ensuring positive tension during all the instants of motion. Then simulations with different initial conditions are presented in order to optimize control parameters, and lastly an experimental validation of the model is carried out, the results are analysed and limits of the presented approach are defined.