Algorithmic mapping of neural network Models onto Parallel SIMD Machines
IEEE Transactions on Computers - Special issue on artificial neural networks
A sublinear-time randomized parallel algorithm for the maximum clique problem in perfect graphs
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
IEEE Transactions on Parallel and Distributed Systems
On the Efficiency of Parallel Backtracking
IEEE Transactions on Parallel and Distributed Systems
Asynchronous Problems on SIMD Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Explicit SIMD Programming for Asynchronous Applications
ASAP '00 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures, and Processors
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ARVLSI '97 Proceedings of the 17th Conference on Advanced Research in VLSI (ARVLSI '97)
MIMD programs on SIMD architectures
FRONTIERS '96 Proceedings of the 6th Symposium on the Frontiers of Massively Parallel Computation
Solving the Maximum Clique Problem using PUBB
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Energy function-based approaches to graph coloring
IEEE Transactions on Neural Networks
Approximating maximum clique with a Hopfield network
IEEE Transactions on Neural Networks
The UCSC Kestrel Parallel Processor
IEEE Transactions on Parallel and Distributed Systems
Expert Systems with Applications: An International Journal
Simulating biological-inspired spiking neural networks with OpenCL
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Improved stochastic competitive Hopfield network for polygonal approximation
Expert Systems with Applications: An International Journal
The Journal of Supercomputing
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Expert Systems with Applications: An International Journal
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Hopfield neural networks are often used to solve difficult combinatorial optimization problems. Multiple restarts versions find better solutions but are slow on serial computers. Here, we study two parallel implementations on SIMD computers of multiple restarts Hopfield networks for solving the maximum clique problem. The first one is a fine-grained implementation on the Kestrel Parallel Processor, a linear SIMD array designed and built the University of California, Santa Cruz. The second one is an implementation on the MasPar MP-2 according to the ''SIMD Phase Programming Model'', a new method to solve asynchronous, irregular problems on SIMD machines. We find that the neural networks map well to the parallel architectures and afford substantial speedups with respect to the serial program, without sacrificing solution quality.