Analysis and evaluation of heuristic methods for static task scheduling
Journal of Parallel and Distributed Computing
A Parallel Genetic Algorithm for Task Mapping on Parallel Machines
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
A Highly Efficient Domain-Programmable Parallel Architecture for Iterative LDPCC Decoding
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Optimizing the Mapping of Low-Density Parity Check Codes on Parallel Decoding Architectures
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Improved low-density parity-check codes using irregular graphs
IEEE Transactions on Information Theory
Research and Simulation of Low Density Parity Check Codes
MACE '12 Proceedings of the 2012 Third International Conference on Mechanic Automation and Control Engineering
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In this paper, we investigate the implementation of iterative probabilistic decoding of low-density parity-check codes on programmable message-passing parallel architectures. We present techniques for optimizing the mapping of tasks to processing units so as to minimize the communication cost by localizing communication. Specifically, we present a simplified clustering technique based on a modified mincut algorithm that reduces the search complexity from quadratic to linear. Cluster allocation is optimized with two different approaches for comparison: using a mincut algorithm and using a genetic algorithm. Results show that the majority of communication locality is exploited by within-cluster communication and is achieved by the clustering operation. The proposed mapping techniques result in a reduction of up to 45% in communication cost compared to random mappings.