Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
Efficient management of parallelism in object-oriented numerical software libraries
Modern software tools for scientific computing
C++ classes for linking optimization with complex simulations
ACM Transactions on Mathematical Software (TOMS)
Parallel Newton-Krylov methods for PDE-constrained optimization
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Uniform Convergence and Mesh Independence of Newton's Method for Discretized Variational Problems
SIAM Journal on Control and Optimization
Toward a Common Component Architecture for High-Performance Scientific Computing
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Globalized Newton-Krylov-Schwarz algorithms and software for parallel implicit CFD
Globalized Newton-Krylov-Schwarz algorithms and software for parallel implicit CFD
Parallel components for PDEs and optimization: some issues and experiences
Parallel Computing - Special issue: Advanced environments for parallel and distributed computing
A Component Architecture for High-Performance Scientific Computing
International Journal of High Performance Computing Applications
Using the GA and TAO toolkits for solving large-scale optimization problems on parallel computers
ACM Transactions on Mathematical Software (TOMS)
An interactive environment for supporting the transition from simulation to optimization
Scientific Programming - POOSC '01 Workshop
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Scientific Programming - Complexity in Scalable Computing
EFCOSS: An interactive environment facilitating optimal experimental design
ACM Transactions on Mathematical Software (TOMS)
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We analyze the performance and scalabilty of algorithms for the solution of large optimization problems on high-performance parallel architectures. Our case study uses the GPCG (gradient projection, conjugate gradient) algorithm for solving bound-constrained convex quadratic problems. Our implementation of the GPCG algorithm within the Toolkit for Advanced Optimization (TAO) is available for a wide range of high-performance architectures and has been tested on problems with over 2.5 million variables. We analyze the performance as a function of the number of variables, the number of free variables, and the preconditioner. In addition, we discuss how the software design facilitates algorithmic comparisons.