Misleading Performance Reporting in tlte Supercomputing Field

ABSTRACT In a previous humorous note, I outlined 12 ways in which performance figures for scientific supercomputers can be distorted. In this paper, the problem of potentially misleading performance reporting is discussed in detail. Included are some examples that have appeared in recent published scientific papers. This paper also includes some proposed guidelines for reporting performance, the adoption of which would raise the level of professionalism and reduce the level of confusion in the field of supercomputing

An Investigation into the Performance Evaluation of Connected Vehicle Applications: From Real-World Experiment to Parallel Simulation Paradigm

A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than tools like METIS, which increase interprocess communications (IPC) overhead by partitioning more transportation corridors. The simulator uses logarithmic accumulation to synchronize parallel simulations, further reducing IPC. Analyses suggest this eliminates up to one-third of IPC overhead incurred by a linear accumulation model

Quantitative performance modeling of scientific computations and creating locality in numerical algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 141-150) and index.by Sivan Avraham Toledo.Ph.D

A methodology for passenger-centred rail network optimisation

Optimising the allocation of limited resources, be they existing assets or investment, is an ongoing challenge for rail network managers. Recently, methodologies have been developed for optimising the timetable from the passenger perspective. However, there is a gap for a decision support tool which optimises rail networks for maximum passenger satisfaction, captures the experience of individual passengers and can be adapted to different networks and challenges. Towards building such a tool, this thesis develops a novel methodology referred to as the Sheffield University Passenger Rail Experience Maximiser (SUPREME) framework. First, a network assessment metric is developed which captures the multi-stage nature of individual passenger journeys as well as the effect of crowding upon passenger satisfaction. Second, an agent-based simulation is developed to capture individual passenger journeys in enough detail for the network assessment metric to be calculated. Third, for the optimisation algorithm within SUPREME, the Bayesian Optimisation method is selected following an experimental investigation which indicates that it is well suited for ‘expensive-to-compute’ objective functions, such as the one found in SUPREME. Finally, in case studies that include optimising the value engineering strategy of the proposed UK High Speed Two network when saving £5 billion initial investment costs, the SUPREME framework is found to improve network performance by the order of 10%. This thesis shows that the SUPREME framework can find ‘good’ resource allocations for a ‘reasonable’ computational cost, and is sufficiently adaptable for application to many rail network challenges. This indicates that a decision support tool developed on the SUPREME framework could be widely applied by network managers to improve passenger experience and increase ticket revenue. Novel contributions made by this thesis are: the SUPREME methodology, an international comparison between the Journey Time Metric and Disutility Metric, and the application of the Bayesian Optimisation method for maximising the performance of a rail network