At Queen Mary, University of London, we have over twenty years of experience in Parallel Computing Applications, mostly on &#x22;massively parallel systems&#x22;, such as the Distributed Array Processors (DAPs). The applications in which we were involved included design of numerical subroutine libraries, Finite Element software, graphics tools, the physics of organic materials, medical imaging, computer vision and more recently, Financial modelling. Two of the projects related to the latter are described in this paper, namely Portfolio Optimisation and Financial Risk Assessment.

Heather M. Liddell

D. Parkinson

G.S. Hodgson

P. Dzwig

Parallel Computing Applications and Financial Modelling

In this paper we discuss a classic clustering algorithm that can be used to segment images and a recently developed active contour image segmentation model. We propose integrating aspects of the classic algorithm to improve the active contour model. For the resulting CVK and B-means segmentation algorithms we examine methods to decrease the size of the image domain. The CVK method has been implemented to run on parallel and distributed computers. By changing the order of updating the pixels, it was possible to replace synchronous communication with asynchronous communication and subsequently the parallel efficiency is improved.

J.R. Rommelse

H.X. Lin

T.F. Chan

Efficient Active Contour and K-Means Algorithms in Image Segmentation

From the model geometry creation to the model analysis, the stages in between such as mesh generation are the most manpower intensive phase in a mesh-based computational mechanics simulation process. On the other hand the model analysis is the most computing intensive phase. Advanced computational hardware and software have significantly reduced the computing time &#x2013; and more importantly the trend is downward. With the kind of models envisaged coming, which are larger, more complex in geometry and modelling, and multiphysics, there is no clear trend that the manpower intensive phase is to decrease significantly in time &#x2013; in the present way of operation it is more likely to increase with model complexity. In this paper we address this dilemma in collaborating components for models in electronic packaging application.

P. Chow

C.-H. Lai

Collaborating Components in Mesh-Based Electronic Packaging

Preface

Algorithms are often parallelized based on data dependence analysis manually or by means of parallel compilers. Some vector/matrix computations such as the matrix-vector products with simple data dependence structures (data parallelism) can be easily parallelized. For problems with more complicated data dependence structures, parallelization is less straightforward. The data dependence graph is a powerful means for designing and analyzing parallel algorithms. However, for sparse matrix computations, parallelization based on solely exploiting the existing parallelism in an algorithm does not always give satisfactory results. For example, the conventional Gaussian elimination algorithm for the solution of a tri-diagonal system is inherently sequential, so algorithms specially for parallel computation has to be designed. After briefly reviewing different parallelization approaches, a powerful graph formalism for designing parallel algorithms is introduced. This formalism will be discussed using a tri-diagonal system as an example. Its application to general matrix computations is also discussed. Its power in designing parallel algorithms beyond the ability of data dependence analysis is shown by means of a new algorithm called ACER (Alternating Cyclic Elimination and Reduction algorithm).

Graph Transformation and Designing Parallel Sparse Matrix Algorithms beyond Data Dependence Analysis

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

Johan Parent

Katja Verbeeck

Jan Lemeire

Ann Nowe

Kris Steenhaut

Erik Dirkx

Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

It is difficult to solve design optimization problems of complex systems by using a traditional computing method because complex simulation processes usually lead to large-scale computation. Therefore the distributed computing technology based on decomposition-coordination theory has received much attention by design engineers. This paper studies a peer-to-peer collaborative optimization method based on distributed computing technology in order to examine flexible optimization. A new distributed collaborative optimization framework is proposed, and a coordination method is developed and used to deal with the conflict of related variables among sub-optimization problems. A multi-agent based distributed computing environment is implemented. The implementation of an optimization agent, in which CORBA technology is used to implement communication between the components of the optimization agent, is discussed in detail. Two examples are used to demonstrate the efficiency of the computing method and the reliability and flexibility of the multi-agent system.

Caijun Xue

Hong Nie

Qingying Qiu

Peien Feng

A Peer-to-Peer Distributed Collaborative Optimization System

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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