From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation

Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds. Method: This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars. Results: A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions. Conclusion: A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table

HPC-GAP: engineering a 21st-century high-performance computer algebra system

Symbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi-core to high-performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes thatdo not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open-source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross-platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi-core nodes, mid-scale distributed clusters of (multi-core) nodes, and full-blown HPC systems, comprising large-scale tightly-connected networks of multi-core nodes. This requires us to develop new cross-layer programming abstractions in the form of new domain-specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high-performance systems comprising up to 32,000 cores, as well as on ubiquitous multi-core systems and distributed clusters. The work reported here paves the way towards full scale exploitation of symbolic computation by high-performance computing systems, and we demonstrate the potential with two major case studies

Software Concerns for Execution on Heterogeneous Platforms

Context: Heterogeneous computing, i.e., computing performed on different types of execution units, such as CPUs, GPUs, FPGAs, has shown to be a feasible path towards higher performance and less energy consumption. Heterogeneous platforms are specialized on specific types of computation, e.g., parallel computing. However, this approach imposes a number of challenges on the software side. One of such challenges is related to software deployment, in which applications must be prepared to be executed in different target architectures. Further, the approach demands a robust inter-process communication solution, since these systems inherently distribute computation. Objective: The objective of this thesis is twofold. First, to provide an overview of the state-of-the-art of software deployment on heterogeneous platforms, with emphasis to goals, concerns, challenges, and identification of topics of importance for further research. Second, to investigate the communication problem and propose a novel method that improves inter-process communication in distributed systems. Method: Six papers were written as results of four studies: (i) a literature review in the form of a systematic mapping study on software deployment on heterogeneous platforms; (ii) a systematic evaluation of deployment methods in the context of a self-driving heavy vehicle; (iii) an investigation on data marshalling approaches and how they perform in the context of a cyber-physical system; and (iv) a novel message restructuring approach, also in the context of cyber-physical systems. Results and Conclusions: The mapping study discussed the (i) concerns on the topic such as scheduling and software quality; the (ii) approaches available, such as frameworks; and the (iii) architecture solutions used, such as styles and principles. In the second study, we found that the performance decay is negligible when using sandboxed environments for deployment. In the third and fourth studies, we proposed and evaluated a data marshalling approach that decreases bandwidth consumption. Future work: We intend to identify challenges that are currently faced in an industrial setting. In particular, a migration from a non-heterogenous platform to a heterogeneous platform can be studied in the context of a modern software development process, such as DevOps

Distributing Real Time Data From a Multi-Node Large Scale Contact Center Using Corba

This thesis researches and evaluates the current technologies available for developing a system for propagation of Real-Time Data from a large scale Enterprise Server to large numbers of registered clients on the network. The large scale Enterprise Server being implemented is a Contact Centre Server, which can be a standalone system or part of a multi-nodal system. This paper makes three contributions to the study of scalable real-time notification services. Firstly, it defines the research of the different technologies and their implementation for distributed objects in today\u27s world of computing. Secondly, the paper explains how we have addressed key design challenges faced when implementing a Notification Service for TAO, which is our CORBA-compliant real-time Object Request Broker (ORB). The paper shows how to integrate and configure CORBA features to provide real-time event communication. Finally, the paper analyzes the results of the implementation and how it compares to existing technologies being used for the propagation of Real-Time Data