Parallelization strategies for molecular simulation using the Monte Carlo algorithm

AbstractIt has been shown that when using a Monte Carlo algorithm to estimate the electrostatic free energy of a biomolecule in a solution, individual random walks can become entrapped in the geometry. We examine a proposed solution, using a sharp restart during the Walk-on-Subdomains step, in more detail. We show that the point at which this solution introduces significant bias is related to properties intrinsic to the molecule being examined. We also examine two potential methods of generating a sharp restart point and show that they both cause no significant bias in the examined molecules and increase the stability of the run times of the individual walks.

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Directional Markov Chain Monte Carlo Algorithm for Fast Dynamic Reliability Assessment

Proceedings of the 2020 The 2nd World Symposium on Software Engineering ◽

10.1145/3425329.3425386 ◽

2020 ◽

Author(s):

Jinling Wang

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Reliability Assessment ◽

Monte Carlo Algorithm ◽

Dynamic Reliability ◽

Fast Dynamic

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A gradient‐based Markov chain Monte Carlo algorithm for elastic pre‐stack inversion with data and model space reduction

Geophysical Prospecting ◽

10.1111/1365-2478.13081 ◽

2021 ◽

Author(s):

Mattia Aleardi

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Model Space ◽

Monte Carlo Algorithm ◽

Space Reduction ◽

Gradient Based

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Bayesian cumulative shrinkage for infinite factorizations

Biometrika ◽

10.1093/biomet/asaa008 ◽

2020 ◽

Vol 107 (3) ◽

pp. 745-752 ◽

Cited By ~ 1

Author(s):

Sirio Legramanti ◽

Daniele Durante ◽

David B Dunson

Keyword(s):

Monte Carlo ◽

Factor Analysis ◽

Monte Carlo Algorithm ◽

Model Complexity ◽

Latent Factors ◽

Shrinkage Process ◽

Shrinkage Prior ◽

Performance Gains ◽

Shrinkage Priors ◽

Model Dimension

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

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Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

BioMed Research International ◽

10.1155/2014/685492 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 12

Author(s):

Kai Xu ◽

Yiwen Wang ◽

Fang Wang ◽

Yuxi Liao ◽

Qiaosheng Zhang ◽

...

Keyword(s):

Monte Carlo ◽

Point Process ◽

Goodness Of Fit ◽

Point Processes ◽

Sequential Monte Carlo ◽

Neuronal Response ◽

Position Estimation ◽

Monte Carlo Algorithm ◽

Sequential Monte Carlo Method ◽

Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

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