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Questions tagged [parallel-computing]

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Parallelizing Rollout/Simulation phase of Monte Carlo Tree Search

I have a Monte Carlo Tree Search implementation that I need to optimize. So I thought about parallelizing the rollout phase. How to do that? Are there any python modules etc that you would recommend?
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1answer
32 views

Parallel Bagging in supervised learning

How can we parallelize Bootstrap aggregation, a.k.a Bagging, i.e. train all classifiers at once?
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1answer
32 views

Training a SOM in batch mode

I was reading the Somoclu parallel implementation of Self-Organizing Maps (SOMs) and they say that in order to make the algorithm parallelizable, a batch training mode has to be followed. The equation ...
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21 views

Computational Optimization of Cross-Validation in Training Models

TL;DR: If I run a 5-fold cross-validation which assigns 1 fold per core when my CPU only has 4 cores, is the 5th as costly (with respect to computation time) as the first 4 folds? Context: I am ...
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158 views

Forecasting large set of time series in R

I have a data set with ~1,000,000 time series which I want to forecast using R. It is monthly data and I have 36 observations of each, and I want to forecast for one year (h=12). Each time series ...
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1answer
73 views

Parallel gradient descent problem

I'm new to ML so please go gentle on me in case I was missing something obvious. I read that GD on parallel machines can be done by splitting points and then averaging the results. However, consider ...
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1answer
381 views

Parallel processing Bayesian model with R

I am recently running a Bayesian model based on DRAM (Delayed Rejection Adaptive Metropolis) sampling on R with FME package. As the analysis is consuming considerable time, I am planning to move it to ...
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1answer
535 views

Summary of manyglm model objects running too slowly in R; can I speed them up?

I have implemented two independent multivariate abundance regressions, both of which use the manyglm function in the mvabund ...
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1answer
1k views

Parallel minibatch gradient descent algorithms

I've implemented a neural network using batch gradient descent. Now I want to try minibatch, largely in order to avoid problems with local minima. In ESL's chapter on neural networks, the authors ...
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162 views

How to implement a SVM on a distributed platform like Hadoop

I am trying to implement a SVM on a distributed computing paltform. More generally, I am asking how to solve a minimax problem distributively. I can not find a way to tackle the ...
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0answers
98 views

Parallelizing SVM

I have a big dataset and implementation of SVM (+ SMO) doesn't support training whole dataset at once, so I have partitioned the dataset into around 20 sets and now its successfully training on all ...
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0answers
48 views

Quantum computing and resampling techniques

Maybe I miss interpreted how does quantum computing work. If I understood well it would allow to perform extreme parallelization by making using a single qubit to perform many calculations at the ...
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1answer
183 views

Parallel Q-learning

I'm looking for academic papers or other credible sources focusing on the topic of parralelized reinforcement learning, specifically Q-learning. I'm mostly interested in methods of sharing Q-table ...
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0answers
130 views

Are there any fast approximations to generalized linear mixed models?

Are there any recommended methods or approximations that would help speed up the estimation of fixed effects components of a generalized linear mixed effects model? Specifically, my dataset includes ...
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1answer
63 views

Possible to train classifiers in parallel on clusters?

Is it possible to train classifiers on data in parallel on supercomputer cluster? I think it makes sense when bagging. But what about in the case of something like SVM? Can this be only done by ...
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1answer
625 views

Estimation of quantile given quantiles of subset

Let's say we have sets $X=\{x_1, x_2, \ldots, x_m\}, Y=\{y_1, y_2, \ldots, y_n\}$ and we have some estimate (or exact) quantile information about them at some level $a$. How could we approximate the ...
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2answers
12k views

Fatal error using `RWeka::NGramTokenizer` with `tm` to build a term document matrix?

I installed the tm library and want to build n-grams of a corpus using the NGramTokenizer from the ...
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1answer
2k views

Combining multiple parallel MCMC chains into one longer chain

Let's say that one has run $m$ parallel MCMC chains where each chain has had burn-in. Let the resulting chains be denoted by $$ x_1^{(i)},\dots,x_N^{(i)} \quad \text{ for } i=1,\dots,m,$$ where $N$ is ...
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0answers
73 views

Parallel association rule mining

I am following papers about parallel association rule mining, in particular, this paper. I do not understand how conditional FP-Tree is generated in the paper, ...
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1answer
777 views

How to apply an R prediction model to very big data from SQL database in parallel.

I dont need to load the entire dataset into memory. In fact I only need 1 row at a time to apply a trained model, get the predicted response and put that response somewhere, possibly back into another ...
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0answers
306 views

Very large dataset with more than 2000 variables descriptive statistics in multicore [closed]

I deal with very large datasets everyday with millions of rows and thousands of columns. I can sample down to do descriptive statistics, but the people i work with here do not believe in sampling ...
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1answer
3k views

step {stats} is too slow. Are there multicore solutions?

I am finding that trying to do a stepwise logistic regression is far too slow on my data set (6 hours). Is anyone aware of any faster solutions out there? Perhaps one that takes advantage of the ...
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2answers
117 views

Strategies for parallelising neural networks

When it comes to parallelising a problem, it involves the division of routines and subroutines between a number of nodes, namely; the master node and the slave nodes. Once each of these nodes ...
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1answer
4k views

Parallel logistic regression

I need to perform stepwise binary logistic regression (The horror! The horror!) on 1.5 million observations. This takes far too long in SAS, so I'm wondering if I can use R to process it in a ...
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1answer
3k views

What is the current 'standard' for modern statistical computing hardware?

I am in the market for a new system (probably a laptop) that would be be used primarily for Bayesian/MCMC analyses. If I had unlimited funds I would obviously buy very high end hardware and be done ...
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0answers
772 views

Fast/parallel alternative to GLS with nlme?

I am using the gls function from nlme to fit a fixed-effects model yet correct for spatial autocorrelation. My dataset has about 100,000 unique geographic observations, and running the following ...
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2answers
66 views

Parallelization of an iterative model

What I have: An iterative process based on the application of a very simple algebra to represent a rate, with total dependence among any iteration and its predecessor (one input parameter for (n)th ...
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1answer
719 views

Simple cloud computing to run R + JAGS simulations

I want to simulate the frequentist properties of a Bayesian model. So, for example, I might want to fit a Bayesian model 1,000 times to 50 different configurations each of which takes about 10 seconds ...
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3answers
920 views

parallelism in data mining softwares

I'm working on a data set for order prediction/classification and a close deadline upcoming. Fortunately, my university has a super-computer with restricted access. I was thinking of using a few nodes ...
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2answers
1k views

Parallel solving Ax=b?

Cross posted on StackOverflow. I have some extremely large sparse matrices created using spMatrix function from the matrix package. Using the solve() function works for my Ax=b issue, but it takes a ...
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0answers
626 views

Physical/pictoral interpretation of higher-order moments

I'm preparing a presentation about parallel statistics. I plan to illustrate the formulas for distributed computation of the mean and variance with examples involving center of gravity and moment of ...
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2answers
3k views

Parallelizing the caret package using doSMP

UPDATE: caret now uses foreach internally, so this question is no longer really relevant. If you can register a working parallel backend for ...
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2answers
588 views

RNG, R, mclapply and cluster of computers

I'm running a simulation on R and a cluster of computers and have the following problem. On each of X computers I run: ...
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4answers
4k views

Who uses R with multicore, SNOW or CUDA package for resource intense computing?

Who of you in this forum uses ">R with the multicore, snow packages, or CUDA, so for advanced calculations that need more power than a workstation CPU? On which hardware do you compute these scripts? ...
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2answers
1k views

Random numbers and the multicore package

When programming in R, I've used the multicore package a few times. However, I've never seen a statement about how it handles it's random numbers. When I use openMP with C, I'm careful to use a proper ...
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2answers
874 views

Gputools for R: how to interpret the experimental procedure?

The following paper describes an implementation of R in parallel on a graphics processing unit (GPU). Buckner et al., The gputools package enables GPU computing in R, BIOINFORMATICS, Vol. 26 no. 1 ...
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5answers
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Any suggestions for making R code use multiple processors?

I have R-scripts for reading large amounts of csv data from different files and then perform machine learning tasks such as svm for classification. Are there any libraries for making use of multiple ...