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Relaxing Synchronization In Distributed Simulated Annealing, Bruce M. Mcmillin, Chul-Eui Hong
Relaxing Synchronization In Distributed Simulated Annealing, Bruce M. Mcmillin, Chul-Eui Hong
Computer Science Faculty Research & Creative Works
This paper presents a cost error measurement scheme and relaxed synchronization method, for simulated annealing on a distributed memory multicomputer, which predicts the amount of cost error that an algorithm will tolerate. An adaptive error control method is developed and implemented on an Intel iPSC/2
Parallel Error Tolerance Scheme Based On The Hill Climbing Nature Of Simulated Annealing, Bruce M. Mcmillin, Chul-Eui Hong
Parallel Error Tolerance Scheme Based On The Hill Climbing Nature Of Simulated Annealing, Bruce M. Mcmillin, Chul-Eui Hong
Computer Science Faculty Research & Creative Works
In parallelizing simulated annealing in a multicomputer, maintaining the global state S involves explicit message traffic and is a critical performance bottleneck. One way to mitigate this bottleneck is to amortize the overhead of these state updates over as many parallel state changes as possible. Using this technique introduces errors in the calculated cost C(S) of a particular state S used by the annealing process. Analytically derived bounds are placed on this error in order to assure convergence to the correct result. The resulting parallel simulated annealing algorithm dynamically changes the frequency of global updates as a function of the …
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Computer Science Faculty Research & Creative Works
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented …
Expectations For Associative-Commutative Unification Speedups In A Multicomputer Environment, Ralph W. Wilkerson, Bruce M. Mcmillin
Expectations For Associative-Commutative Unification Speedups In A Multicomputer Environment, Ralph W. Wilkerson, Bruce M. Mcmillin
Computer Science Faculty Research & Creative Works
An essential element of automated deduction systems is unification algorithms which identify general substitutions and when applied to two expressions, make them identical. However, functions which are associative and commutative, such as the usual addition and multiplication functions, often arise in term rewriting systems, program verification, the theory of abstract data types and logic programming. The introduction to the associative and commutative equality axioms together with standard unification brings with it problems of termination and unreasonably large search spaces. One way around these problems is to remove the troublesome axioms from the system and to employ a unification algorithm which …