Tom Peachey

NIMROD/O: A TOOL FOR AUTOMATIC DESIGN OPTIMISATION USING PARALLEL AND DISTRIBUTED SYSTEMS

This paper describes a novel tool called Nimrod/O that allows a user to run an arbitrary computational model as the core of a non-linear optimization process. Nimrod/O allows a user to specify the domain and type of parameters to the model, and also a specification of which output variable is to be minimized or maximized. Accordingly, a user can formulate a question like: “what parameter settings will minimize the model output?”. Nimrod/O currently employs a number of built-in optimization algorithms, namely BFGS, Simplex, Divide and Conquer and Simulated Annealing. Jobs can be executed on a variety of platforms, including distributed clusters and Computational Grid resources. The paper demonstrates the utility of the system with a number of case studies.

An Automatic Design Optimization Tool and its Application to Computational Fluid Dynamics

In this paper we describe the Nimrod/O design optimization tool, and its application in computational fluid dynamics. Nimrod/O facilitates the use of an arbitrary computational model to drive an automatic optimization process. This means that the user can parameterise an arbitrary problem, and then ask the tool to compute the parameter values that minimize or maximise a design objective function. The paper describes the Nimrod/O system, and then discusses a case study in the evaluation of an aerofoil problem. The problem involves computing the shape and angle of attack of the aerofoil that maximises the lift to drag ratio. The results show that our general approach is extremely flexible and delivers better results than a program that was developed specifically for the problem. Moreover, it only took us a few hours to set up the tool for the new problem and required no software development.

Model optimization and parameter estimation with nimrod/o

Optimization problems where the evaluation step is computationally intensive are becoming increasingly common in both engineering design and model parameter estimation. We describe a tool, Nimrod/O, that expedites the solution of such problems by performing evaluations concurrently, utilizing a range of platforms from workstations to widely distributed parallel machines. Nimrod/O offers a range of optimization algorithms adapted to take advantage of parallel batches of evaluations. We describe a selection of case studies where Nimrod/O has been successfully applied, showing the parallelism achieved by this approach.

Fractional factorial design for parameter sweep experiments using Nimrod/E

The techniques of formal experimental design and analysis are powerful tools for scientists and engineers. However, these techniques are currently underused for experiments conducted with computer models. This has motivated the incorporation of experimental design functionality into the Nimrod tool chain. Nimrod has been extensively used for exploration of the response of models to their input parameters; the addition of experimental design tools will combine the efficiency of carefully designed experiments with the power of distributed execution. This paper describes the incorporation of one type of design, the fractional factorial design, and associated analysis tools, into the Nimrod framework. The result provides a convenient environment that automates the design of an experiment, the execution of the jobs on a computational grid and the return of results, and which assists in the interpretation of those results. Several case studies are included which demonstrate various aspects of this approach.

An Evolutionary Programming Algorithm for Automatic Engineering Design

This paper describes a new Evolutionary Programming algorithm based on Self-Organised Criticality. When tested on a range of problems drawn from real-world applications in science and engineering, it performed better than a variety of gradient descent, direct search and genetic algorithms. It proved capable of delivering high quality results faster, and is simple, robust and highly parallel.

Mixing Grids and Clouds: High-Throughput Science Using the Nimrod Tool Family

The Nimrod tool family facilitates high-throughput science by allowing researchers to explore complex design spaces using computational models. Users are able to describe large experiments in which models are executed across changing input parameters. Different members of the tool family support complete and partial parameter sweeps, numerical search by non-linear optimisation and even workflows. In order to provide timely results and to enable large-scale experiments, distributed computational resources are aggregated to form a logically single high-throughput engine. To date, we have leveraged grid middleware standards to spawn computations on remote machines. Recently, we added an interface to Amazon’s Elastic Compute Cloud (EC2), allowing users to mix conventional grid resources and clouds. A range of schedulers, from round-robin queues to those based on economic budgets, allow Nimrod to mix and match resources. This provides a powerful platform for computational researchers, because they can use a mix of university-level infrastructure and commercial clouds. In particular, the system allows a user to pay money to increase the quality of the research outcomes and to decide exactly how much they want to pay to achieve a given return. In this chapter, we will describe Nimrod and its architecture, and show how this naturally scales to incorporate clouds. We will illustrate the power of the system using a case study and will demonstrate that cloud computing has the potential to enable high-throughput science.

Leveraging e-Science infrastructure for electrochemical research

As in many scientific disciplines, modern chemistry involves a mix of experimentation and computer-supported theory. Historically, these skills have been provided by different groups, and range from traditional ‘wet’ laboratory science to advanced numerical simulation. Increasingly, progress is made by global collaborations, in which new theory may be developed in one part of the world and applied and tested in the laboratory elsewhere. e-Science, or cyber-infrastructure, underpins such collaborations by providing a unified platform for accessing scientific instruments, computers and data archives, and collaboration tools. In this paper we discuss the application of advanced e-Science software tools to electrochemistry research performed in three different laboratories – two at Monash University in Australia and one at the University of Oxford in the UK. We show that software tools that were originally developed for a range of application domains can be applied to electrochemical problems, in particular Fourier voltammetry. Moreover, we show that, by replacing ad-hoc manual processes with e-Science tools, we obtain more accurate solutions automatically.

Enhancing and developing the practical optimisation capabilities and intelligence of automatic design software

In the modern engineering design cycle the use of computational tools becomes a necessity. The complexity of the engineering systems under consideration for design increases dramatically as the demands for advanced and innovative design concepts and engineering products is expanding. At the same time the advancements in the available technology in terms of computational resources and power, as well as the intelligence of the design software, accommodate these demands and make them a viable approach towards the challenge of real-world engineering problems. This class of design optimisation problems is by nature multi-disciplinary. In the present work we establish enhanced optimisation capabilities within the Nimrod/O tool for massively distributed execution of computational tasks through cluster and computational grid resources, and develop the potential to combine and benefit from all the possible available technological advancements, both software and hardware. We develop the interface between a Free Form Deformation geometry management in-house code with the 2D airfoil aerodynamic efficiencyevaluation tool XFoil, and the well established multi-objective heuristic optimisation algorithm NSGA-II. A simple airfoil design problem has been defined to demonstrate the functionality of the design system, but also to accommodate a framework for future developments and testing with other state-of-the-art optimisation algorithms such as the Multi-Objective Genetic Algorithm (MOGA) and the Multi-Objective Tabu Search (MOTS) techniques. Ultimately, heavily computationally expensive industrial design cases can be realised within the presented framework that could not be investigated before.

High-throughput cardiac science on the Grid

Cardiac electrophysiology is a mature discipline, with the first model of a cardiac cell action potential having been developed in 1962. Current models range from single ion channels, through very complex models of individual cardiac cells, to geometrically and anatomically detailed models of the electrical activity in whole ventricles. A critical issue for model developers is how to choose parameters that allow the model to faithfully reproduce observed physiological effects without over-fitting. In this paper, we discuss the use of a parametric modelling toolkit, called Nimrod, that makes it possible both to explore model behaviour as parameters are changed and also to tune parameters by optimizing model output. Importantly, Nimrod leverages computers on the Grid, accelerating experiments by using available high-performance platforms. We illustrate the use of Nimrod with two case studies, one at the cardiac tissue level and one at the cellular level.

RSCS: a parallel simplex algorithm for the Nimrod/O optimization toolset

This paper describes a method of parallelisation of the popular Nelder-Mead simplex optimization algorithms that can lead to enhanced performance on parallel and distributed computing resources. A reducing set of simplex vertices are used to derive search directions generally closely aligned with the local gradient. When tested on a range of problems drawn from real-world applications in science and engineering, this reducing set concurrent simplex (RSCS) variant of the Nelder-Mead algorithm compared favourably with the original algorithm, and also with the inherently parallel multidirectional search algorithm (MDS). All algorithms were implemented and tested in a general-purpose, grid-enabled optimization toolset.