ASETS – An Academic Trading Simulation Platform

This paper is intended to present the results of our academic research upon a distributed computing environment dedicated to trading simulation. Our research has been conducted with the aim of creating a trading simulation platform, that would provide both the foundation for future experiments with trading systems architectures, components, APIs, and the framework for research on trading strategies, trading algorithms design, and equity markets analysis tools.Trading Systems, Simulation, Distributed Computing, Service-Oriented Architecture (SOA), Message-Oriented Middleware (MOM), Java Message Service (JMS)

Panel on future challenges in modeling methodology

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

Developing a grid computing system for commercial-off-the-shelf simulation packages

Today simulation is becoming an increasingly pervasive technology across major business sectors. Advances in COTS Simulation Packages and Commercial Simulation Software have made it easier for users to build models, often of large complex processes. These two factors combined are to be welcomed and when used correctly can be of great benefit to organisations that make use of the technology. However, it is also the case that users hungry for answers do not always have the time, or possibly the patience, to wait for results from multiple replications and multiple experiments as standard simulation practice would demand. There is therefore a need to support this advance in the use of simulation within today’s business with improved computing technology. Grid computing has been put forward as a potential commercial solution to this requirement. To this end, Saker Solutions and the Distributed Systems Research Group at Brunel University have developed a dedicated Grid Computing System (SakerGrid) to support the deployment of simulation models across a desktop grid of PCs. The paper identifies route taken to solve this challenging issue and suggests where the future may lie for this exciting integration of two effective but underused technologies

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Distributed simulation and the grid: Position statements

The Grid provides a new and unrivaled technology for large scale distributed simulation as it enables collaboration and the use of distributed computing resources. This panel paper presents the views of four researchers in the area of Distributed Simulation and the Grid. Together we try to identify the main research issues involved in applying Grid technology to distributed simulation and the key future challenges that need to be solved to achieve this goal. Such challenges include not only technical challenges, but also political ones such as management methodology for the Grid and the development of standards. The benefits of the Grid to end-user simulation modelers also are discussed

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in terms of computing resources. Thus new approaches to event generation and simulation of detector responses are needed. In LHCb, the accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU time. An alternative approach is described here, when one generates high-level reconstructed observables using a generative neural network to bypass low level details. This network is trained to reproduce the particle species likelihood function values based on the track kinematic parameters and detector occupancy. The fast simulation is trained using real data samples collected by LHCb during run 2. We demonstrate that this approach provides high-fidelity results.Comment: Proceedings for 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. (Fixed typos and added one missing reference in the revised version.