Importance sampling for Ising computers using one-dimensional cellular automata

The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configu...

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Veröffentlicht in:	IEEE transactions on computers Jg. 38; H. 6; S. 769 - 774
Hauptverfasser:	Hortensius, P.D., Card, H.C., McLeod, R.D., Pries, W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.1989 Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithmics. Computability. Computer arithmetics Applied sciences Computational modeling Computer architecture Computer science; control theory; systems Concurrent computing Exact sciences and technology Monte Carlo methods Pervasive computing Physics computing Signal processing algorithms Systolic arrays Temperature Theoretical computing Very large scale integration Monte Carlo method Parallel algorithm Cellular automaton VLSI circuit Computer architecture Algorithmics Pseudorandom number Parallelism Random number generation
ISSN:	0018-9340
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Abstract	The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially distributed set of pseudorandom numbers that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence.< >
AbstractList	The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially distributed set of pseudorandom numbers that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence.< > The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially distributed set of pseudorandom numbers that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially distributed set of pseudorandom numbers that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence. (I.E.)
Author	Card, H.C. Pries, W. McLeod, R.D. Hortensius, P.D.
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Keywords	Monte Carlo method Parallel algorithm Cellular automaton VLSI circuit Computer architecture Algorithmics Pseudorandom number Parallelism Random number generation
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References	ref13 denyer (ref1) 1985 ref12 ref15 ref14 burks (ref5) 1970 ref11 binder (ref10) 1979 ref16 ref19 kung (ref2) 1985 ref18 hortensius (ref25) 1987 verhangen (ref17) 1976; 15 knuth (ref26) 1981 wolfram (ref8) 1984 ref24 ref23 codd (ref6) 1968 ref20 ref22 ref21 ref28 kung (ref3) 1980 ref27 ref29 ref7 ref9 ref4
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SubjectTerms	Algorithmics. Computability. Computer arithmetics Applied sciences Computational modeling Computer architecture Computer science; control theory; systems Concurrent computing Exact sciences and technology Monte Carlo methods Pervasive computing Physics computing Signal processing algorithms Systolic arrays Temperature Theoretical computing Very large scale integration
Title	Importance sampling for Ising computers using one-dimensional cellular automata
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