A Mapping Strategy for Parallel Processing
IEEE Transactions on Computers
A Partitioning Strategy for Nonuniform Problems on Multiprocessors
IEEE Transactions on Computers
Nearest-neighbor mapping of finite element graphs onto processor meshes
IEEE Transactions on Computers
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Heuristic approaches to task allocation for parallel computing
Heuristic approaches to task allocation for parallel computing
Load balancing loosely synchronous problems with a neural network
C3P Proceedings of the third conference on Hypercube concurrent computers and applications: Architecture, software, computer systems, and general issues - Volume 1
Introduction to algorithms
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Nonlinear adaptive finite element systems on distributed memory computers
EDMCC2 Proceedings of the 2nd European conference on Distributed memory computing
Performance of dynamic load balancing algorithms for unstructured mesh calculations
Concurrency: Practice and Experience
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
//ELLPACK: a numerical simulation programming environment for parallel MIMD machines
ICS '90 Proceedings of the 4th international conference on Supercomputing
A multilevel algorithm for partitioning graphs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Dynamic load balancing for adaptive meshes using symmetric broadcast networks
ICS '98 Proceedings of the 12th international conference on Supercomputing
Parallel multilevel k-way partitioning scheme for irregular graphs
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Parallel Processing of Adaptive Meshes with Load Balancing
IEEE Transactions on Parallel and Distributed Systems
A new approach to parallel mesh generation and partitioning problems
Computational science, mathematics and software
Efficient large-scale BGP simulations
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Network modelling and simulation
Performance analysis of dynamic workflow scheduling in multicluster grids
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Mapping communication layouts to network hardware characteristics on massive-scale blue gene systems
Computer Science - Research and Development
Hi-index | 0.00 |
Mapping data to parallel computers aims at minimizing the execution time of the associated application. However, it can take an unacceptable amount of time in comparison with the execution time of the application if the size of the problem is large. In this paper, first we motivate the case for graph contraction as a means for reducing the problem size. We restrict our discussion to applications where the problem domain can be described using a graph (e.g., computational fluid dynamics applications). Then we present a mapping-oriented Parallel Graph Contraction (PGC) heuristic algorithm that yields a smaller representation of the problem to which mapping is then applied. The mapping solution for the original problem is obtained by a straight-forward interpolation. We then present experimental results on using contracted graphs as inputs to two physical optimization methods; namely, Genetic Algorithm and Simulated Annealing. The experimental results show that the PGC algorithm still leads to a reasonably good quality mapping solutions to the original problem, while producing a substantial reduction in mapping time. Finally, we discuss the cost-quality tradeoffs in performing graph contraction.