Using distributed memory parallel computers and GPU clusters for multidimensional Monte Carlo integration

2014 ◽  
Vol 27 (4) ◽  
pp. 923-936 ◽  
Author(s):  
Dominik Szałkowski ◽  
Przemysław Stpiczyński
Keyword(s):  
Monte Carlo ◽  
Distributed Memory ◽  
Parallel Computers ◽  
Monte Carlo Integration ◽  
Gpu Clusters

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

scholarly journals Control variate selection for Monte Carlo integration

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Rémi Leluc ◽  
François Portier ◽  
Johan Segers
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Integration ◽  
Control Variate ◽  
Selection For

scholarly journals Studies of Electron-Molecule Collisions on Distributed-memory Parallel Computers

1992 ◽  
Vol 45 (3) ◽  
pp. 325 ◽  
Author(s):  
Carl Winstead ◽  
Qiyan Sun ◽  
Paul G Hipes ◽  
Marco AP Lima ◽  
Vincent McKoy
Keyword(s):  
Distributed Memory ◽  
Recent Progress ◽  
Cost Effective ◽  
Parallel Computers ◽  
Polyatomic Molecules ◽  
Low Energy ◽  
Materials Processing ◽  
Challenging Problem ◽  
Molecular Systems ◽  
Critical Data

We review recent progress in the study of low-energy collisions between electrons and polyatomic molecules which has resulted from the application of distributed-memory parallel computing to this challenging problem. Recent studies of electronically elastic and inelastic scattering from several molecular systems, including ethene, propene, cyclopropane, and disilane, are presented. We also discuss the potential of ab initio methods combined with cost-effective parallel computation to provide critical data for the modeling of materials-processing plasmas.

scholarly journals Estimating effective population size from samples of sequences: a bootstrap Monte Carlo integration method

1992 ◽  
Vol 60 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Joseph Felsenstein
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Population Size ◽  
Effective Population Size ◽  
Random Mating ◽  
Computational Effort ◽  
Monte Carlo Integration ◽  
Bootstrap Sampling ◽  
Effective Population ◽  
Full Data

SummaryWe would like to use maximum likelihood to estimate parameters such as the effective population size Ne, or, if we do not know mutation rates, the product 4Neμof mutation rate per site and effective population size. To compute the likelihood for a sample of unrecombined nucleotide sequences taken from a random-mating population it is necessary to sum over all genealogies that could have led to the sequences, computing for each one the probability that it would have yielded the sequences, and weighting each one by its prior probability. The genealogies vary in tree topology and in branch lengths. Although the likelihood and the prior are straightforward to compute, the summation over all genealogies seems at first sight hopelessly difficult. This paper reports that it is possible to carry out a Monte Carlo integration to evaluate the likelihoods pproximately. The method uses bootstrap sampling of sites to create data sets for each of which a maximum likelihood tree is estimated. The resulting trees are assumed to be sampled from a distribution whose height is proportional to the likelihood surface for the full data. That it will be so is dependent on a theorem which is not proven, but seems likely to be true if the sequences are not short. One can use the resulting estimated likelihood curve to make a maximum likelihood estimate of the parameter of interest, Ne or of 4Neμ. The method requires at least 100 times the computational effort required for estimation of a phylogeny by maximum likelihood, but is practical on today's work stations. The method does not at present have any way of dealing with recombination.

Sign in / Sign up

Export Citation Format

Share Document