Improving Parallel Ordering of Sparse Matrices Using Genetic Algorithms

Parallel Genetic Algorithms for Constrained Ordering Problems

This paper proposes two different parallel genetic algorithms (PGAs) for constrained ordering problems. Constrained ordering problems are constraint optimization problems (COPs) for which it is possible represent a candidate solution as a permutation of objects. A decoder is used to decode this permutation into an instantiafion of the COP vm-iables. Two examples of such constrmnsd ordering prob...

High-Performance Graph Algorithms from Parallel Sparse Matrices

Large–scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High– performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing. We argue that many of the tools of high-performance numerical computing – in particular, ...

Ordering Sparse Matrices for Cache-Based Systems

The Multidisciplinary Design Optimization of a Reentry Vehicle Using Parallel Genetic Algorithms

The purpose of this paper is to examine the multidisciplinary design optimization (MDO) of a reentry vehicle. In this paper, optimization of a RV based on, minimization of heat flux integral and minimization of axial force coefficient integral and maximization of static margin integral along reentry trajectory is carried out. The classic optimization methods are not applicable here due to the c...

Parallel vision algorithms using sparse array representations

Sparse arrays are arrays in which the number of non-zero elements is a small fraction of the total number of array elements. This paper presents computer vision algorithms using sparse representations for arrays. The parallel architecture considered is a hypercube. The algorithms can be easily modified for other architectures like the mesh. We assume that the architecture is SIMD, i.e., all PEs...