Recent development and perspectives of machines for lattice QCD

I highlight recent progress in cluster computer technology and assess status and prospects of cluster computers for lattice QCD with respect to the development of QCDOC and apeNEXT. Taking the LatFor test case, I specify a 512-processor QCD-cluster better than 1$/Mflops.Comment: 14 pages, 17 figures, Lattice2003(plenary

apeNEXT: A Multi-Tflops LQCD Computing Project

This paper is a slightly modified and reduced version of the proposal of the {\bf apeNEXT} project, which was submitted to DESY and INFN in spring 2000. .It presents the basic motivations and ideas of a next generation lattice QCD (LQCD) computing project, whose goal is the construction and operation of several large scale Multi-TFlops LQCD engines, providing an integrated peak performance of tens of TFlops, and a sustained (double precision) performance on key LQCD kernels of about 50% of peak speed

A dynamic systems approach to risk assessment in megaprojects

Purpose- Megaprojects are large, complex, and expensive projects that often involve social, technical, economic, environmental and political (STEEP) challenges. Despite these challenges, project owners and financiers continue to invest large sums of money in megaprojects that run high risks of being over schedule and over budget. While some degree of cost, schedule and quality risks are considered during planning, the challenge of understanding how risk interactions and impacts on project performance can be modelled dynamically still remains. The consequences learnt from past experiences indicate that there was a lack of dynamic tools to manage such risks effectively in megaproject construction. In seeking to help address these problems, this research put forward an innovative dynamic systems approach called SDANP to risk assessment in megaprojects construction. Design/methodology/approach – The research has developed an innovative SDANP method which involves an integrative use of system dynamics (SD) and analytic network process (ANP) for risk assessment. The SDANP model presented in the thesis has been testified by using data and information collected through a questionnaire survey and interviews from supply-side stakeholders involved in the Edinburgh Tram Network (ETN) project at the Phase One of its construction stage. The SDANP method is a case study risk assessment driven process and can be used against STEEP challenges in megaprojects. Findings – The result of the case study project revealed that the SDANP method is an effective tool for risk assessment to support supply-side stakeholders in decision making in construction planning. The SDANP model has demonstrated its efficiency through case study, and has convinced construction practitioners in terms of its innovation and usefulness. Research limitations/implications – Although the SDANP model has been developed for generic use in risk assessment, data and information used to run the simulation were based on the ETN project, which is in Edinburgh, Scotland. The use of the SDANP model in other megaprojects requires further data and information from local areas. Practical implications – The SDANP method provides an innovative approach to a comprehensive dynamic risk assessment of STEEP issues at the construction planning stage of megaprojects for the first time. It provides an interactive quantitative way for developers to prioritise and simulate potential risks across the project supply network, to understand and predict in advance the consequences of STEEP risks on project performance at the construction stage. Originality/value - The research made an original contribution in quantitative risk assessment with regard to the need for a methodological innovation in research and for a powerful sophisticated tool in practice. The SDANP has shown its advantages over existing tools such as the program evaluation and review technique (PERT) and the risk assessment matrix (RAM)

Factory-installation of software on workstations and servers

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 69).by H. Earl Hones, III.S.M

Causality and sensitivity analysis in distributed design simulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2002.Includes bibliographical references (leaves 109-111).Numerous collaborative design frameworks have been developed to accelerate the product development, and recently environments for building distributed simulations have been proposed. For example, a simulation framework called DOME (Distributed Object-oriented Modeling and Evaluation) has been developed in MIT CADlab. DOME is unique in its decentralized structure that allows heterogeneous simulations to be stitched together while allowing proprietary information an simulation models to remain secure with each participant. While such an approach offers many advantages, it also hides causality and sensitivity information, making it difficult for designers to understand problem structure and verify solutions. The purpose of this research is to analyze the relationships between design parameters (causality) and the strength of the relationships (sensitivity) in decentralized web-based design simulation. Algorithms and implementations for the causality and sensitivity analysis are introduced. Causality is determined using Granger's definition of causality, which is to distinguish causation from association using conditional variance of the suspected output variable. Sensitivity is estimated by linear regression analysis and a perturbation method, which transfers the problem into a frequency domain by generating periodic perturbations. Varying Internet latency and disturbances are issues with these methods. Thus, algorithms are developed and tested to overcome these problems.by Jaehyun Kim.Ph.D