Grid Enabled Surrogate Modeling

Grid Enabled Surrogate Modeling

Dirk Gorissen (Gent University–IBBT, Belgium), Tom Dhaene (Gent University–IBBT, Belgium), Piet Demeester (Gent University–IBBT, Belgium) and Jan Broeckhove (Gent University–IBBT, Belgium)
DOI: 10.4018/978-1-60566-184-1.ch025
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Abstract

The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling methods are often used for the efficient exploration of the design space, as they reduce the number of simulations needed. However, constructing such surrogate models (or metamodels) is often done in a straightforward, sequential fashion. In contrast, this chapter presents a framework that can leverage the use of compute clusters and grids in order to decrease the model generation time by efficiently running simulations in parallel. The authors describe the integration between surrogate modeling and grid computing on three levels: resource level, scheduling level and service level. This approach is illustrated with a simple example from aerodynamics.
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Introduction

Computer based simulation has become an integral part of the engineering design process. Rather than building real world prototypes and performing experiments, application scientists can build a computational model and simulate the physical processes at a fraction of the original cost. However, despite the steady growth of computing power, the computational cost to perform these complex, high-fidelity simulations are still enormous. A simulation may take many minutes, hours, days or even weeks (Gu, 2001; Lin et al., 2005; Qian et al., 2006). This is especially evident for routine tasks such as optimization, sensitivity analysis and design space exploration as noted below:

“...it is reported that it takes Ford Motor Company about 36-160 hrs to run one crash simulation. For a two-variable optimization problem, assuming on average 50 iterations are needed by optimization and assuming each iteration needs one crash simulation, the total computation time would be 75 days to 11 months, which is unacceptable in practice” (Wang and Shan, 2007, p1).

Consequently, scientists have turned towards upfront approximation methods to reduce simulation times. The basic approach is to construct a simplified approximation of the computationally expensive simulator, which is then used in place of the original code to facilitate Multi-Objective Design Optimization (MDO), design space exploration, reliability analysis, and so on (Simpson, 2004). Since the approximation model acts as surrogate for the original code, it is referred to as a surrogate model or metamodel.

While the time needed for one evaluation of the original simulator is typically in the order of minutes or hours, the surrogate function, due to its compact mathematical notation, can be evaluated in the order of milliseconds. However, in order to construct an accurate surrogate one still requires evaluations of the original objective function, thus cost remains an issue. The focus of this paper is to discuss one technique to reduce this cost even further using distributed computing. By intelligently running simulations in parallel, the “wall-clock” execution time, in order to come to an acceptable surrogate model can be considerably reduced.

We present a framework that integrates the automated building of surrogate models with the distributed evaluation of the simulator. This integration occurs on multiple levels: resource level, scheduling level and the service level. Each of these will be detailed below.

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Background

Surrogate Modeling

Surrogate models play a significant role in many disciplines (hydrology, automotive industry, robotics, ...) where they help bridge the gap between simulation and understanding. The principal reason driving their use is that the simulator is too time consuming to run for a large number of simulations. A second reason is when simulating large scale systems, for example: a full-wave simulation of an electronic circuit board. Electro-magnetic modeling of the whole board in one run is almost intractable. Instead the board is modeled as a collection of small, compact, accurate surrogates that represent different functional components (capacitors, resistors, etc.) on the board.

There are a huge number of different surrogate model types available, with applications in domains ranging from medicine, ecology, economics to aerodynamics. Depending on the domain, popular model types include Radial Basis Function (RBF) models, Rational Functions, Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Kriging models (Wang and Shan, 2007).

An important aspect of surrogate modeling is sample selection. Since data is computationally expensive to obtain, it is impossible to use traditional, one-shot, full factorial or space filling designs. Data points must be selected iteratively, there where the information gain will be the greatest (Kleijnen, 2005). A sampling function is needed that minimizes the number of sample points selected in each iteration, yet maximizes the information gain of each sampling step. This process is called adaptive sampling, but is also known as active learning, Optimal Experimental Design (OED), and sequential design.

Key Terms in this Chapter

Workflow: A workflow is a set of tasks (=nodes) that process data in a structured and systematic manner. In the case that each node is implemented as a service, a workflow describes how the services interact and exchange data in order to solve a higher level problem.

Experimental Design: The theory of Design of Experiments (DOE) describes methods and algorithms for optimally selecting data points from an n dimensional parameter space. A simple example, say you have to select 1000 points from a 3-dimensional space (n=3). This can be done randomly, using a full factorial design (an equal number of points in every dimension, i.e., 10x10x10) or according to a Latin hypercube. These are 3 basic examples of an experimental design.

Meta-Scheduler: A meta-scheduler is a software layer that abstracts the details of different grid middlewares. In this way a client can support multiple submission systems while only having to deal with one protocol (that used by the abstraction layer). An example of a meta-scheduler is GridWay (www.gridway.org).

Middleware: The middleware is responsible for managing the grid resources (access control, job scheduling, resource registration and discovery, etc.), abstracting away the details and presenting the user with a consistent, virtual computer to work with. Examples of middlewares include: Globus, Unicore, Legion and Triana.

Service Oriented Architecture (SOA): SOA represents an architectural model in which functionality is decomposed into small, distinct units (services), which can be distributed over a network and can be combined together and reused to create applications.

Surrogate Model: This is a model that approximates a more complex, higher order model and used in place of the complex model (hence the term surrogate). The reason is usually that the complex model is too computationally expensive to use directly, hence the need for a faster approximation. It is also known as a response surface model or a metamodel.

Sequential Design: For a high number of dimensions, n > 3, it quickly becomes impossible to use traditional space filling experimental designs since the number of points needed grows exponentially. Instead data points must be chosen iteratively and intelligently, there where the information gain is the highest. This process is known as sequential design, adaptive sampling or active learning.

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Table of Contents
Foreword
Ruth E. Shaw
Preface
Emmanuel Udoh, Frank Zhigang Wang
Acknowledgment
Emmanuel Udoh
Chapter 1
Emmanuel Udoh, Frank Zhigang Wang, Vineet R. Khare
This chapter presents a historical record of the advent of Grid with a recourse to some basic definitions commonly accepted by most researchers. It... Sample PDF
Overview of Grid Computing
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Chapter 2
Eric Aubanel
The problem of load balancing parallel applications is particularly challenging on computational grids, since the characteristics of both the... Sample PDF
Resource-Aware Load Balancing of Parallel Applications
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Chapter 3
Enis Afgan, Purushotham Bangalore
Grid computing has emerged as the next generation computing platform. Because of the resource heterogeneity that exists in the grid environment... Sample PDF
Assisting Efficient Job Planning and Scheduling in the Grid
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Chapter 4
Kuo-Chan Huang, Po-Chi Shih, Yeh-Ching Chung
Most current grid environments are established through collaboration among a group of participating sites which volunteer to provide free computing... Sample PDF
Effective Resource Allocation and Job Scheduling Mechanisms for Load Sharing in a Computational Grid
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Chapter 5
Tevfik Kosar
As the data requirements of scientific distributed applications increase, the access to remote data becomes the main performance bottleneck for... Sample PDF
Data-Aware Distributed Batch Scheduling
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Chapter 6
Gianni Pucciani, Flavia Donno, Andrea Domenici, Heinz Stockinger
Data replication is a well-known technique used in distributed systems in order to improve fault tolerance and make data access faster. Several... Sample PDF
Consistency of Replicated Datasets in Grid Computing
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Chapter 7
Ming Wu, Xian-He Sun
Rapid advancement of communication technology has changed the landscape of computing. New models of computing, such as business-on-demand, Web... Sample PDF
Quality of Service of Grid Computing
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Chapter 8
QoS in Grid Computing  (pages 75-83)
Zhihui Du, Zhili Cheng, Xiaoying Wang, Chuang Lin
This chapter first summarizes popular terms of QoS related concepts and technologies in grid computing, including SLA, End-to-End QoS Provision and... Sample PDF
QoS in Grid Computing
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Chapter 9
Kris Bubendorfer, Ben Palmer, Ian Welch
A Grid resource broker is the arbiter for access to a Grid’s computational resources and therefore its performance and functionality has a... Sample PDF
Trust and Privacy in Grid Resource Auctions
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Chapter 10
Sandro Fiore, Alessandro Negro, Salvatore Vadacca, Massimo Cafaro, Giovanni Aloisio, Roberto Barbera
Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed... Sample PDF
An Architectural Overview of the GRelC Data Access Service
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Chapter 11
Man Wang, Zhihui Du, Zhili Cheng
Resource Management System (RMS), which manages the Grid resources and matches the applications’ requests to the proper resources, is one of the... Sample PDF
Adaptive Resource Management in Grid Environment
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Chapter 12
Vineet R. Khare, Frank Zhigang Wang
The need for a dynamic and scalable expansion of the grid infrastructure and resources and other scalability issues in terms of execution efficiency... Sample PDF
Bio-Inspired Grid Resource Management
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Chapter 13
Yuhui Deng, Frank Zhigang Wang, Na Helian
Storage Grid is a new model for deploying and managing the heterogeneous, dynamic, large-scale, and geographically distributed storage resources.... Sample PDF
Service Oriented Storage System Grid
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Chapter 14
Dominic Cherry, Maozhen Li, Man Qi
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Chapter 15
Maozhen Li, Man Qi, Bin Yu
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale... Sample PDF
Service Discovery with Rough Sets
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Chapter 16
Irfan Habib, Ashiq Anjum, Richard McClatchey
Due to some barriers to adoption we have not seen a proliferation of Grid Computing technologies throughout e-Science or other domains. This chapter... Sample PDF
On the Pervasive Adoption of Grid Technologies: A Grid Operating System
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Chapter 17
Kurt Vanmechelen, Jan Broeckhove, Wim Depoorter, Khalid Abdelkader
As grid computing technology moves further up the adoption curve, the issues of dealing with conflicting user requirements formulated by different... Sample PDF
Pricing Computational Resources in Grid Economies
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Chapter 18
Rosario M. Piro
Large, geographically distributed and heterogeneous computing infrastructures, such as the Grid, often span multiple organizations and... Sample PDF
Resource Usage Accounting in Grid Computing
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Chapter 19
Frans Arickx, Jan Broeckhove, Peter Hellinckx, David Dewolfs, Kurt Vanmechelen
Quantum structure or scattering calculations often belong to a class of computational problems involving the aggregation of a set of matrices... Sample PDF
Grid-Based Nuclear Physics Applications
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Chapter 20
Gabriel Aparicio, Fernando Blanco, Ignacio Blanquer, César Bonavides, Juan Luis Chaves, Miguel Embid, Álvaro Hernández
In the last years an increasing demand for Grid Infrastructures has resulted in several international collaborations. This is the case of the EELA... Sample PDF
Developing Biomedical Applications in the Framework of EELA
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Chapter 21
Gerald Schaefer, Roger Tait
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Chapter 22
Daniele Andreotti, Armando Fella, Eleonora Luppi
The BaBar experiment uses data since 1999 in examining the violation of charge and parity (CP) symmetry in the field of high energy physics. This... Sample PDF
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Chapter 23
Diego Liberati
A framework is proposed that creates, uses, and communicates information, whose organizational dynamics allows performing a distributed cooperative... Sample PDF
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Chapter 24
Roberto Barbera, Valeria Ardizzone, Leandro Ciuffo
The Grid INFN virtual Laboratory for Dissemination Activities (GILDA) is a fully working Grid test-bed devoted to training and dissemination... Sample PDF
Grid INFN Virtual Laboratory for Dissemination Activities (GILDA)
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Chapter 25
Dirk Gorissen, Tom Dhaene, Piet Demeester, Jan Broeckhove
The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling... Sample PDF
Grid Enabled Surrogate Modeling
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Chapter 26
Patrik Skogster
Grid computing is becoming as essential part of different business analysis. In traditional business computing infrastructures data transfer occurs... Sample PDF
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Chapter 27
Gokop Goteng, Ashutosh Tiwari, Rajkumar Roy
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Chapter 28
Salvatore Scifo
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Accessing Grid Metadata through a Web Interface
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Chapter 29
Jyotsna Sharma
Efforts in Grid Computing, both in academia and industry, continue to grow rapidly worldwide for research, scientific and commercial purposes.... Sample PDF
Grid Computing Initiatives in India
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Chapter 30
Hai Jin, Li Qi, Jie Dai, Yaqin Luo
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