TUTORIAL DESCRIPTION
In 1995, in a seminal paper (R. B. Schnabel, "A View of the Limitations, Opportunities, and Challenges in Parallel
Nonlinear Optimization", Parallel Computing, 21(6), 1995, pp. 875905), three main aspects of highperformance
and parallel global optimization were described: (1) Parallelizing the objective function calculation;
(2) Parallelizing the underlying numerical libraries and kernels; and (3) Redesigning the algorithm for increased
parallelism. The primary focus of the proposed tutorial is the third aspect, as well as completely new paradigms
specifically designed for highperformance computation.
New applications of global optimization abound. For example, complex phenomena are often modeled as large
systems of equations, and model parameters must be determined to correspond with experimental data. Global
optimization is used to determine these parameters. Furthermore, many important engineering problems rely on
simulationbased optimization, wherein the cost function itself is formed from the results of large simulation
experiments. In these cases, closedform derivatives of the objective function are generally not available,
and are not easily computed. Therefore, new optimization paradigms must be considered to solve these problems.
TUTORIAL OUTLINE
The proposed tutorial would include the following topics:

Introduction to the optimization problem and a brief overview of its classical, serial solutions.


Parallel techniques in local and global optimization.


 
Finegrained approaches: Parallelization of derivative computation and cost function computation.


 
Coarsegrained approaches: Searching different parts of the search space simultaneously.


The intrinsic parallelism of deterministic global methods, including DIRECT, branch and bound, and interval


Stochastic and computational intelligence methods, including simulated annealing, genetic algorithms,


evolutionary computation, and a special emphasis on particle swarm optimization.


Emerging computer architectures for, and applications in, highperformance global optimization.


 
Simulationbased optimization, where derivative information is not available, and the cost of computing



each objective function value is very high. Important applications include safety engineering and the design



of materials. Highperformance derivativefree optimization methods will be discussed.


 
Highperformance computing approaches to determine the optimal parameters of mathematical models



that provide the best fit between observed and estimated data. Calibrating a model to observed data



generally improves the model’s predictive capabilities, and also provides a means for model verification and improvement.



improvement.


 
Biomedical applications, particularly in imaging, computerguided surgery and therapy, bioinformatics, and



proteomics.

TARGET AUDIENCE
The target audience includes researchers, students, and practitioners who require optimization for solving large, complex problems. Specifically, those who work in simulation and modeling learn about optimizationbased simulation, its implementation, and potential applications. Optimization for parameter estimation will also be discussed.
REQUIRED BACKGROUND
Although some mathematical background and some knowledge of parallel computing and computing algorithms is helpful, the tutorial will focus on applied concepts and parallelization techniques rather than on theory.
DURATION
About three hours would be needed for this tutorial. Given three hours, a tentative breakdown would be:

Hour 1 – The optimization problem and some traditional approaches. Applications to simulation and modeling, and medical imaging.


Hour 2 – Coarsegrained parallel optimization approaches, specifically geared towards optimizationbased simulation and parameter estimation.


Hour 3 – Parallelization strategies and future applications.

INSTRUCTOR BIOGRAPHY
Dr. Mark Wachowiak is currently an Assistant Professor at Nipissing University in North Bay, Canada. He has worked as a Postdoctoral Fellow and Research Associate at Robarts Research Institute in London, Canada, where he helped plan and build a supercomputing facility in the Imaging Laboratories. He also held an adjunct appointment in the Department of Medical Biophysics at the University of Western Ontario, London, Canada. He obtained the Doctorate degree from the University of Louisville, USA, in 2002, and was awarded the Best Dissertation Award for his work in particle swarm optimization.
Dr. Wachowiak’s research interests are highperformance computing and parallel algorithms in scientific computing, grid computing, biomedical applications including imaging, bioinformatics, proteomics, and systems biology, and parallel global optimization. His recent work has focused on parallel optimization techniques for medical image alignment. He has been an invited speaker at several highperformance computing conferences.