Parallelization of Plasma Physics Simulations on Massively Parallel Architectures

Proyecto de Graduación (Maestría en Ingeniería en Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2017.Clean energy sources have increased its importance in the last few years. Because of that, the seek for more sustainable sources has been increased too. This effect made to turn the eyes of the scientific community into plasma physics, specially to the controlled fusion. This plasma physics developments have to rely on computer simulation processes before start the implementation of the respective fusion devices. The simulation process has to be done in order to detect any kind of issues on the theoretical model of the device, saving time and money. To achieve this, those computer simulation processes have to finish in a timely manner. If not, the simulation defeats its purpose. However, in recent years, computer systems have passed from an increment speed approach to a increment parallelism approach. That change represents a short stop for these applications. Because of these reasons, on this dissertation we took one plasma physics application for simulation and sped it up by implementing vectorization, shared, and distributed memory programming in a hybrid model. We ran several experiments regarding the performance improvement and the scaling of the new implementation of the application on sumpercomputers using a recent architecture, Intel Xeon Phi - Knights Landing - manycore processor. The claim of this thesis is that a plasma physics application can be parallelized achieving around 0.8 of performance under the right configuration and the right architecture

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Virtual Service Provisioning for Internet of Things Applications in Mobile Edge Computing

The Internet of Things (IoT) paradigm is paving the way for many new emerging technologies, such as smart grid, industry 4.0, connected cars, smart cities, etc. Mobile Edge Computing (MEC) provides promising solutions to reduce service delays for delay-sensitive IoT applications, where cloudlets are co-located with wireless access points in the proximity of IoT devices. Most mobile users have specified Service Function Chain (SFC) requirements, where an SFC is a sequence of Virtual Network Functions (VNFs). Meanwhile, edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed on the local IoT device of the request; and the other part is processed on a cloudlet (server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet in which the request is assigned. In this thesis, we will focus on virtual service provisioning for IoT applications in MEC Environments. Firstly, we consider the user satisfaction problem of using services jointly provided by an MEC network and a remote cloud for delay-sensitive IoT applications, through maximizing the accumulative user satisfaction when different user services have different service delay requirements. A novel metric to measure user satisfaction of using a service is proposed, and efficient approximation and online algorithms for the defined problems under both static and dynamic user service demands are then devised and analyzed. Secondly, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements with the aim of minimizing service provisioning cost, where each IoT application has multiple data streams from different sources to be uploaded to the MEC network for processing and storage, while each data stream must pass through the network functions of the SFC of the IoT application, prior to reaching its destination. A service provisioning framework for such multi-source IoT applications is proposed, through uploading stream data from multiple IoT sources, VNF instance placement and sharing, in-network aggregation of data streams, and workload balancing among cloudlets. Efficient algorithms for service provisioning of multi-source IoT applications in MEC networks, built upon the proposed framework, are also proposed. Thirdly, we investigate a novel DNN inference throughput maximization problem in an MEC network with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and inference parallelism. We devise a constant approximation algorithm for the problem under the offline setting, and an online algorithm with a provable competitive ratio for the problem under the online setting, respectively. Fourthly, we address a robust SFC placement problem with the aim to maximize the expected profit collected by the service provider of an MEC network, under the assumption of both computing resource and data rate demand uncertainties. We start with a special case of the problem where the measurement of the expected demanded resources for each request admission is accurate, under which we propose a near-optimal approximation algorithm for the problem by adopting the Markov approximation technique, which can achieve a provable optimality gap. Then, we extend the proposed approach to the problem of concern, for which we show that the proposed algorithm still is applicable, and the solution delivered has a moderate optimality gap with bounded perturbation errors on the profit measurement. Finally, we summarize the thesis work and explore several potential research topics that are based on the studies in this thesis

Drift orbits and neoclassical transfort in the H-1NF heliac

This thesis is concerned with neoclassical transport in the H-1NF heliac, and contains an examination of drift-orbit geometries, a description of a neoclassical Monte Carlo transport code, and a description of a method to use that code to self-consistently calculate ambipolar radial electric fields. We set out to study the contributions to neoclassical transport in H-1NF, by first describing the topology and the abundance of collisionless, trapped particle orbits in the presence of radial electric fields. We give an overview of the trapped orbit geometries in H-1NF, and develop a method to numerically classify the trapped particle orbits. On average, the trapped particle fraction in H-1NF is 40%, with approximately 5%, 15%, and 20% of the orbits in the deeply trapped, helically trapped, and toroidally trapped states, respectively. A condensed version of this component of the thesis has been submitted to Nuclear Fusion. The orbit studies provide a background for the development of a neoclassical Monte Carlo transport code, MCMuPPeT (for Monte Carlo, Multi Processing Plasma Transport). Using the code, we compare several Monte Carlo transport diagnostics, taken from the literature. Confinement times and diffusion coefficients are calculated for plasma conditions which will be achievable in H-1NF after the National Facility upgrade. Since the electric field can dominate in the determination of the transport, we develop an iterative method to self-consistently calculate the ambipolar radial electric field, using the Monte Carlo code. The method is applied to the Argon plasma conditions observed in H-1NF, in the experimentally observed Improved Conhnement Mode (ICM). To help interpret the results, the ambipolar electric fields were calculated in the same conditions using a well-known analytic model which was geometrically-fitted to H-1NF for our purposes. Qualitative agreement was found between both of the neoclassical models and the experimental results; the electric fields predicted in the ICM conditions are typically twice as large as those predicted in the conditions before the transition. The two models were also used to look for the neoclassically predicted transition from negative to positive radial electric field. Positive radial electric fields were observed, at long mean free path, in Hydrogen plasma conditions which will be achievable in H-1NF after the National Facility upgrade. We have also developed methods to optimise the Monte Carlo code for both parallel and vector computing environments. Two Message Passing algorithms that we use to parallelise the MC code are presented in the appendix