A Scalable Parallel Formulation of the Backpropagation Algorithm for Hypercubes and Related Architectures

Authors:
V. Kumar;S. Shekhar;M. B. Amin
Affiliations:
-;-;-
Venue:
IEEE Transactions on Parallel and Distributed Systems
Year:
1994

Citing 15
Cited 10

Topological Properties of Hypercubes

IEEE Transactions on Computers
Explorations in parallel distributed processing: a handbook of models, programs, and exercises

Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Parallel and distributed computation: numerical methods

Parallel and distributed computation: numerical methods
Mapping neural networks onto message-passing multicomputers

Journal of Parallel and Distributed Computing - Neural Computing
A unified systolic architecture for artificial neural networks

Journal of Parallel and Distributed Computing - Neural Computing
Optimum Broadcasting and Personalized Communication in Hypercubes

IEEE Transactions on Computers
Hypercube algorithms: with applications to image processing and pattern recognition

Hypercube algorithms: with applications to image processing and pattern recognition
Scalability of parallel algorithms for the all-pairs shortest-path problem

Journal of Parallel and Distributed Computing
Algorithmic mapping of neural network Models onto Parallel SIMD Machines

IEEE Transactions on Computers - Special issue on artificial neural networks
Introduction to parallel computing: design and analysis of algorithms

Introduction to parallel computing: design and analysis of algorithms
Analyzing scalability of parallel algorithms and architectures

Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
A Survey of Parallel Machine Organization and Programming

ACM Computing Surveys (CSUR)
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures

IEEE Parallel & Distributed Technology: Systems & Technology
Generalization by Neural Networks

IEEE Transactions on Knowledge and Data Engineering
The Scalability of FFT on Parallel Computers

IEEE Transactions on Parallel and Distributed Systems

Algorithmic Mapping of Feedforward Neural Networks onto Multiple Bus Systems

IEEE Transactions on Parallel and Distributed Systems
Practical aspects and experiences Scalable massively parallel algorithms for computational nanoelectronics

Parallel Computing
A scalable parallel algorithm for training a hierarchical mixture of neural experts

Parallel Computing
Strategies for Parallel Data Mining

IEEE Concurrency
A Distributed Discrete-Time Neural Network Architecture for Pattern Allocation and Control

IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Parallel Implementation of Back-Propagation Algorithm in Networks of Workstations

IEEE Transactions on Parallel and Distributed Systems
Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms

Cluster Computing
Design and Implementation of Parallel Counterpropagation Networks Using MPI

Informatica
Artificial neural networks in hardware: A survey of two decades of progress

Neurocomputing
Parallel implementation of back-propagation neural network software on SMP computers

PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new technique for mapping the backpropagation algorithm on hypercubeand related architectures. A key component of this technique is a network partitioningscheme called checkerboarding. Checkerboarding allows us to replace the all-to-allbroadcast operation performed by the commonly used vertical network partitioningscheme, with operations that are much faster on the hypercubes and relatedarchitectures. Checkerboarding can be combined with the pattern partitioning techniqueto form a hybrid scheme that performs better than either one of these schemes.Theoretical analysis and experimental results on nCUBE and CM5 show that our schemeperforms better than the other schemes, for both uniform and nonuniform networks.