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Relevant bibliographies by topics / Monte carlo PCA for parallel analysis

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Academic literature on the topic 'Monte carlo PCA for parallel analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 8 July 2025

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Journal articles on the topic "Monte carlo PCA for parallel analysis"

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Vaculíkova, Jitka. "The Third Round of the Czech Validation of the Motivated Strategies for Learning Questionnaire (MSLQ)." International Education Studies 9, no. 7 (2016): 35. http://dx.doi.org/10.5539/ies.v9n7p35.

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<p class="apa">The authors present findings on the third round of the Czech validation of the Motivated Strategies for learning questionnaire (MSLQ), originally developed by Pintrich et al. (1991). The validation only covered an area designed to access motivation in self-regulated learning. Data was collected from a sample of university students in regular classroom settings. Principal component analysis (PCA) was conducted with eigenvalues exceeding 1. An inspection of the scree plot, discontinuity in variance, Monte Carlo parallel analysis and Cronbach’s alphas were performed to assess the psychometric properties. The results were further supported by the confirmatory factor analysis with no post hoc model modifications needed. The analysis confirmed the first and second round validation structure bringing a 3-factor model and indicated that the revised MSLQ is an acceptable measure of motivation in self-regulated learning.</p>

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Teixeira, José Eduardo, Pedro Forte, Ricardo Ferraz, et al. "Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach." PeerJ 11 (August 4, 2023): e15806. http://dx.doi.org/10.7717/peerj.15806.

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Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

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Shahla, Siddiqui, and Fatima Samreen. "Multivariate techniques to Pomadasys maculatum (Bloch) of Karachi Coast, Pakistan | JBES 2020 @zenodo." Journal of Biodiversity and Environmental Sciences (JBES) 17, no. 1 (2020): 78–84. https://doi.org/10.5281/zenodo.5807567.

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This paper aims to investigate the variability in the morphometric and meristic characteristics in a set of fishery data by applying multivariate statistical techniques; PCA and Factor analysis. Moreover, the gender-wise difference in these variables is also investigated via a statistical independent t-test. Empirical analysis result obtained from the t-test shows that the total length of the body in females is 8.079, whereas the male has 8.054 of the whole body length. Furthermore, a test of independent results of the body displays that the weight of females and males are 9.966 and 10.053 respectively. However, head length and snout length are found 9.143 and 10.568 in females and in males 09.145 and 10.557 respectively. To sum up, all the variables are slightly significant in both (Male and Female). In the second phase via applying PCA, it is found that the first three variables (total length (TL), Bodyweight (But), Standard length (SL)) are retained 90.047% of the total variation.

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Teixeira, José Eduardo, Luís Branquinho, Ricardo Ferraz, et al. "Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach." Sports 12, no. 7 (2024): 194. http://dx.doi.org/10.3390/sports12070194.

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Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019–2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed.

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Kaçak, Tugay, and Abdullah Faruk Kılıç. "Factor extraction in exploratory factor analysis for ordinal indicators: Is principal component analysis the best option?" International Journal of Assessment Tools in Education 12, no. 1 (2025): 113–30. https://doi.org/10.21449/ijate.1481201.

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Researchers continue to choose PCA in scale development and adaptation studies because it is the default setting and overestimates measurement quality. When PCA is utilized in investigations, the explained variance and factor loadings can be exaggerated. PCA, in contrast to the models given in the literature, should be investigated in categorical/ordered, severely skewed data, and multidimensional structures. The purpose of this study is to compare the relative bias and percent correct estimation of PCA, PAF, and MINRES techniques with Monte Carlo simulations. In Monte Carlo simulations sample size, level of skewness, number of categories, average factor loadings, number of factors, level of inter-factor correlation and test length were manipulated. The results show that PCA overestimates most models with lower average factor loadings, but PAF and MINRES provide unbiased results even with low factor loadings. PAF and MINRES produce more accurate and impartial results, and it is projected that PCA will lead researchers to believe that the items in scale development or adaptation studies are of "high quality."

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Bhavsar, V. C., and J. R. Isaac. "Design and Analysis of Parallel Monte Carlo Algorithms." SIAM Journal on Scientific and Statistical Computing 8, no. 1 (1987): s73—s95. http://dx.doi.org/10.1137/0908014.

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Alexandrov, V. N., I. T. Dimov, A. Karaivanova, and C. J. K. Tan. "Parallel Monte Carlo algorithms for information retrieval." Mathematics and Computers in Simulation 62, no. 3-6 (2003): 289–95. http://dx.doi.org/10.1016/s0378-4754(02)00252-5.

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Ihme, Susann, Hartmut Döhner, Konstanze Döhner, Michaela Feuring-Buske, Christian Buske, and Medhanie A. Mulaw. "Two Long Non-Coding RNAs Are Sufficient to Classify and Significantly Predict In Vivo Engraftment Potential and LSC Properties of Human Acute Myeloid Leukemia Cells." Blood 128, no. 22 (2016): 2880. http://dx.doi.org/10.1182/blood.v128.22.2880.2880.

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Abstract Acute Myeloid Leukemia (AML) is a hematological malignancy with heterogeneous genetics and clinical course. Studies have shown that there is a strong correlation between xenograft in vivo engraftment potential and clinical outcome. Furthermore, such studies have conducted gene expression profiling to identify gene signatures associated with engraftment potential, leukemic stem cell (LSC) property, and prognosis. Most of these analyses were limited to protein coding genes. More recently, advances in next generation sequencing (NGS) have created a paradigm shift on our perception of transcription that, contrary to previous consensus, the human genome is largely transcribed and most of these transcripts do not code for proteins (ncRNA). In the current work, we conducted a comprehensive functional genomics study to identify long ncRNA (lncRNA) that significantly predict in vivo engraftment potential and LSC properties of AML and evaluate their functional and prognostic relevance. We first sorted normal granulocyte-macrophage progenitor (GMP), lymphoid-primed multi-potential progenitor (LMPP) and CD34-subpopulations from leukemic bulk of 15 AML patient samples. Sorted samples were then transplanted into the non-obese diabetic/severe combined immunodeficient Gamma (NSG) mouse model. Only the subpopulations showing robust leukemic engraftment (> 1%) were classified as LSC populations. In parallel, we performed total RNA based RNA-seq using Ovation® Single Cell RNA-Seq System (NuGEN Technologies Inc.) and evaluated both protein coding genes and lncRNAs. In line with previous studies, we observed that the engraftment potential primarily resides within the CD34+ subpopulations (GMPs and LMPPs). LMPPs/GMPs from 8 AML patients engrafted (defined as LSC populations), while subpopulations from the other 7 patients failed to engraft (non-LSC fraction). Using our RNA-seq data, we first conducted a global principal component analysis (PCA) based between group analysis (BGA) based on lncRNA expression levels. Interestingly, we noted that the LMPPs and GMPs primarily cluster based on their engraftment potential rather than cell type. In two subsequent independent analyses, we employed weighted correlation network analysis (WGCNA) and top scoring pairs (tsp) to identify lncRNAs that show significant correlation to engraftment percentages and could also categorically classify samples based on their engraftment status. Results of the WGCNA analysis showed that 830 lncRNAs are statistically significantly correlated to engraftment percentages of subpopulations (p < 0.05). In the tsp analysis, by taking all lncRNAs in the dataset in a pairwise fashion, we identified two lncRNAs that could statistically significantly classify samples into 'LSC' (engrafters) and 'non-LSCs' (non-engrafters) (mean classification error ~ 0.19 using bootstrap analysis; statistically significant with p-value < 0.0001 using Monte Carlo Simulation). Of interest here, the two lncRNAs were also in the list of significantly correlated lncRNAs based on the separately conducted WGCNA. Combining the two analyses and using a 3d plot, we looked at the expression levels of two lncRNAs and engraftment percentages of the subpopulations and were able to see that the GMP/LMPP LSCs formed a distinct cluster. The non-LSCs with GMP or LMPP phenotype were distantly clustered from their engrafting counterparts while all the CD34-negative non-LSCs clustered with no overlap to the other groups. Furthermore, a guilt-by-association analysis was carried out to identify potential functional link of the candidate lncRNAs by assessing their correlation to functionally annotated genes. 'JAK-STAT', 'Hematopoietic lineage', and 'Toll-like receptor signaling' were some of the significant pathways (p < 0.05; FDR < 0.25). We also tested some published AML LSC and engraftment associated signatures. The lncRNAs showed negative correlation to the Eppert et al. (2011; Nat. Med.) LSC signature and set of lineage affiliated genes (Goardon et al. 2011; Cancer Cell) including FLT3, NOTCH1, and RUNX1. Taken together, our approach shows that the two lncRNAs we identified sufficiently recapitulate the underlying engraftment potential of AMLs and predict LSC property with significant accuracy. We conclude that these findings highlight the necessity to focus on lncRNAs as key players in clinical and functional studies of AML. Disclosures Buske: Celltrion, Inc.: Consultancy, Honoraria. Mulaw:NuGEN: Honoraria.

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Xue, Jingnan, and Faming Liang. "Double-Parallel Monte Carlo for Bayesian analysis of big data." Statistics and Computing 29, no. 1 (2017): 23–32. http://dx.doi.org/10.1007/s11222-017-9791-1.

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Campillo, Fabien, Rivo Rakotozafy, and Vivien Rossi. "Parallel and interacting Markov chain Monte Carlo algorithm." Mathematics and Computers in Simulation 79, no. 12 (2009): 3424–33. http://dx.doi.org/10.1016/j.matcom.2009.04.010.

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Dissertations / Theses on the topic "Monte carlo PCA for parallel analysis"

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Cruz, Manuel Ernani de Carvalho. "A parallel Monte-Carlo partial-differential-equation procedure for the analysis of multicomponent random media." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12334.

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Solat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.

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The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals.<br>Ph. D.

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Manalo, Kevin. "Detailed analysis of phase space effects in fuel burnup/depletion for PWR assembly & full core models using large-scale parallel computation." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50351.

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Nuclear nonproliferation research and forensics have a need for improved software solutions, particularly in the estimates of the transmutation of nuclear fuel during burnup and depletion. At the same time, parallel computers have become effectively sized to enable full core simulations using highly-detailed 3d mesh models. In this work, the capability for modeling 3d reactor models is researched with PENBURN, a burnup/depletion code that couples to the PENTRAN Parallel Sn Transport Solver and also to the Monte Carlo solver MCNP5 using the multigroup option. This research is computationally focused, but will also compare a subset of results of experimental Pressurized Water Reactor (PWR) burnup spectroscopy data available with a designated BR3 PWR burnup benchmark. Also, this research will analyze large-scale Cartesian mesh models that can be feasibly modeled for 3d burnup, as well as investigate the improvement of finite differencing schemes used in parallel discrete ordinates transport with PENTRAN, in order to optimize runtimes for full core transport simulation, and provide comparative results with Monte Carlo simulations. Also, the research will consider improvements to software that will be parallelized, further improving large model simulation using hybrid OpenMP-MPI. The core simulations that form the basis of this research, utilizing discrete ordinates methods and Monte Carlo methods to drive time and space dependent isotopic reactor production using the PENBURN code, will provide more accurate detail of fuel compositions that can benefit nuclear safety, fuel management, non-proliferation, and safeguards applications.

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Ruengvirayudh, Pornchanok. "A Monte Carlo Study of Parallel Analysis, Minimum Average Partial, Indicator Function, and Modified Average Roots for Determining the Number of Dimensions with Binary Variables in Test Data: Impact of Sample Size and Factor Structure." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou151516919677091.

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Bhering, Felipe Lunardi. "Confiabilidade em sistemas coerentes: um modelo bayesiano Weibull." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-01122013-155316/.

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O principal objetivo desse trabalho é introduzir um modelo geral bayesiano Weibull hierárquico para dados censurados que estima a função de confiabilidade de cada componente para sistemas de confiabilidade coerentes. São introduzidos formas de estimação mais sólidas, sem a inserção de estimativas médias nas funções de confiabilidade (estimador plug-in). Através desse modelo, são expostos e solucionados exemplos na área de confiabilidade como sistemas em série, sistemas em paralelo, sistemas k-de-n, sistemas bridge e um estudo clínico com dados censurados intervalares. As soluções consideram que as componentes tem diferentes distribuições, e nesse caso, o sistema bridge ainda não havia solução na literatura. O modelo construído é geral e pode ser utilizado para qualquer sistema coerente e não apenas para dados da área de confiabilidade, como também na área de sobrevivência, dentre outros. Diversas simulações com componentes com diferentes proporções de censura, distintas médias, três tipos de distribuições e tamanhos de amostra foram feitas em todos os sistemas para avaliar a eficácia do modelo.<br>The main purpose of this work is to introduce a general bayesian Weibull hierarchical model for censored data which estimates each reliability components function from coherent systems. Its introduced estimation procedures which do not consider plug-in estimators. Also, its exposed and solved with this model examples in reliability area such as series systems, parallel systems, k-out-of-n systems, bridge systems and a clinical study with interval censoring data. The problem of bridge system hadnt a solution before for the case of each component with different distribution. Actually, this model is general and can be used to analyse any kind of coherent system and censored data, not only reliability ones, but also survival data and others. Several components simulations with different censored proportions, distinct means, three kinds of distributions and sample size were made in all systems to evaluate model efficiency.

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Parker, Jason Mascagni Michael. "Extensions and optimizations to the scalable, parallel random number generators library." 2003. http://etd.lib.fsu.edu/theses/available/etd-11182003-021957.

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Thesis (M.S.)--Florida State University, 2003.<br>Advisor: Michael Mascagni, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Mar. 2, 2004). Includes bibliographical references.

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Book chapters on the topic "Monte carlo PCA for parallel analysis"

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Shi, Guoyong, Sheldon X. D. Tan, and Esteban Tlelo Cuautle. "Statistical Parallel Monte-Carlo Analysis on GPUs." In Advanced Symbolic Analysis for VLSI Systems. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1103-5_12.

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Winkler, Gerhard. "Parallel Algorithms." In Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-97522-6_11.

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Winkler, Gerhard. "Partially Parallel Algorithms." In Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6_9.

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Cai, Chang-qing, Xiao-ping Ren, Guo-dong Hao, Jian Wang, and Tao Huang. "Statistical Analysis of CCM.M-K1 International Comparison Based on Monte Carlo Method." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49956-7_12.

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Kozikowski, Grzegorz, and Bartłomiej Jacek Kubica. "Parallel Approach to Monte Carlo Simulation for Option Price Sensitivities Using the Adjoint and Interval Analysis." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55195-6_57.

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"Parallel Computing." In Markov Chain Monte Carlo Simulations and Their Statistical Analysis. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812703637_0006.

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Quack, M., and M. A. Suhm. "Potential energy surfaces, quasiadiabatic channels, rovibrational spectra, and intramolecular dynamics of (HFh and its isotopomers from quantum Monte Carlo calculations." In Quantum Monte Carlo. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.0075.

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Abstract The hydrogen fluoride dimer (HF)2 is the simplest molecule exhibiting the phenomenon of a hydrogen bond. In this case, the hydrogen fluoride molecules are loosely held together by the relatively weak hydrogen bond to produce a floppy molecule with anharmonic vibrations not easily characterized, but producing an interesting and complex spectroscopy. In this paper, Quack and Suhm report the use of the diffusion QMC method in analyzing the behavior of the dimer and its isotopomers, and the successful prediction and assignment of spectral bands observed in their earlier experiments.a The DQMC calculations were carried out for nuclear motion on two different six-dimensional potential energy surfaces adjusted to reproduce experimentally measured spectra including anharmonic interactions between all vibrational modes. Two new QMC methods for calculating excited rotational and vibrational states were introduced: a clamped-coordinate quasiadiabatic channel and a centrifugal-energy approximation scheme. In general, quantitative agreement with experimental observations was obtained, and the success of DQMC methods in the prediction and analysis of the spectra of hydrogen-bonded systems was clearly demonstrated. The large differences in electron and proton masses led to extensive computation requirements due to the slow equilibration and serial correlation induced by the heavier protons. The calculations were executed on one of the first massively parallel computers, a Thinking Machines CM-2 with 65,536 processors. Frequent communication among the processors was required to balance the number of walkers treated in each.

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Liu, Jun S., JunniL Zhang, Michael J. Palumbo, and Charles E. Lawrence. "Bayesian Clustering with Variable and Transformation Selections." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0014.

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Abstract The clustering problem has attracted much attention from both statisticians and computer scientists in the past 50 years. Methods such as hierarchical clustering and the K-means method are convenient and competitive first choices off the shelf for the scientist. Gaussian mixture modelling is another popular but computationally expensive clustering strategy, especially when the data are high-dimensional. We propose to first conduct a principal component analysis (PCA) or correspondence analysis (CA) for dimension reduction, and then fit Gaussian mixtures to the data projected to the several major PCA or CA directions. Two technical difficulties of this approach are: (a) the selection of a subset of the PCA factors that are informative for clustering, and (b) the selection of a proper transformation for each factor. We propose a Bayesian formulation and Markov chain Monte Carlo strategies that overcome the two difficulties and examine the performances of the new method by both simulation studies and real applications in molecular imaging analysis and DNA microarray analysis.

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Deussen J., Riehme J., and Naumann U. "Automation of Significance Analyses with Interval Splitting." In Advances in Parallel Computing. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-621-7-731.

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In the SCoRPiO project we are interested in the significance of program code with regard to its outputs in order to compute less significant parts for instance on less reliable but power saving hardware. Multiple approaches can be taken including pure interval arithmetics [1], Monte Carlo methods, and a tool chain for interval derivative based significance analysis. The tool chain dco/scorpio [2,3] was introduced in the SCoRPiO project. In this paper we propose to automate the process of interval derivative based significance analysis to widen input domains. We present an interval splitting approach to handle difficulties introduced by the interval evaluation, e.g. unfeasible relational operators or the wrapping effect. Each split will result in multiple scenarios which can be computed in parallel. The presented approach is a step forward towards a fully automatic significance analysis of computer code.

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Lara-Molina Fabian Andres, Koroishi Edson Hideki, and Bolzon Victor. "Stochastic Analysis of the Kinematic Performance of a Planar 5R Symmetrical Parallel Mechanism." In Advances in Transdisciplinary Engineering. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-703-0-563.

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This paper aims at studying the kinematic model of the 5R symmetrical parallel mechanism considering the uncertainties in the lengths of links and the clearances in the active joints. The complete kinematic model of the 5R parallel mechanism is formulated in the presence of the uncertain parameters that are model as random variables. The stochastic analysis, based on the Monte Carlo Method, permits to evaluate numerically the performance of the mechanism when the uncertain parameters are considered. Thus, the kinematic model for several poses within the workspace is evaluated. Moreover, the variability in the workspace produced by the uncertain parameters is also analyzed.

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Conference papers on the topic "Monte carlo PCA for parallel analysis"

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Sato, F., A. V. Garcia, and A. Monticelli. "Parallel implementation of probabilistic short-circuit analysis by the Monte Carlo approach." In Conference Proceedings Power Industry Computer Application Conference. IEEE, 1993. http://dx.doi.org/10.1109/pica.1993.290993.

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WILMOTH, RICHARD. "Direct simulation Monte Carlo analysis on parallel processors." In 24th Thermophysics Conference. American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1666.

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Ferrero, E. E., A. B. Kolton, and M. Palassini. "Parallel kinetic Monte Carlo simulation of Coulomb glasses." In INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4893513.

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Koyanagi, Mitsumasa, Tamio Shimatani, Takuji Matsumoto, Kee-Ho Yu, Yasushi Yoshida, and Reiji Aibara. "New parallel processor system with optical interconnection specific for Monte Carlo analysis." In Photonics West '95, edited by Ray T. Chen and Harvard S. Hinton. SPIE, 1995. http://dx.doi.org/10.1117/12.206308.

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Qavamy, Zahra, Behnam Ghavami, Morteza Nabavi, and Yvon Savaria. "Non-parametric Statistical Static Timing Analysis based on Improved Parallel Monte Carlo." In 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2021. http://dx.doi.org/10.1109/mwscas47672.2021.9531688.

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Ahn, Jungil, Jinwook Kim, and Young Hwan Kim. "Statistical leakage analysis by parallel Monte-Carlo programming on a CUDA platform." In 2011 3rd Asia Symposium on Quality Electronic Design (ASQED 2011). IEEE, 2011. http://dx.doi.org/10.1109/asqed.2011.6111717.

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LAFARGE, ROBERT. "Using Monte Carlo techniques and parallel processing for fragmentation analysis of explosive payloads." In 30th Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-653.

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Mbazor, Jeremiah, and Marco Torbol. "Large Scale Parallel Markov Chain Monte Carlo For Reliability analysis of Complex Systems." In Proceedings of the 8th International Conference on Computational Stochastic Mechanics (CSM 8). Research Publishing Services, 2018. http://dx.doi.org/10.3850/978-981-11-2723-6_36-cd.

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Zhang, Haiqiang, Jianglong Tang, Kan Shi, and Yan'an Yao. "Accuracy analysis of three degrees of parallel mechanism based on Monte Carlo Simulation." In International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), edited by Qiang Zhang and Zhong You. SPIE, 2021. http://dx.doi.org/10.1117/12.2625290.

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Ozog, David, Allen D. Malony, and Andrew R. Siegel. "A Performance Analysis of SIMD Algorithms for Monte Carlo Simulations of Nuclear Reactor Cores." In 2015 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2015. http://dx.doi.org/10.1109/ipdps.2015.105.

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Reports on the topic "Monte carlo PCA for parallel analysis"

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LaFarge, R. Using Monte Carlo techniques and parallel processing for debris hazard analysis of rocket systems. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10127805.

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Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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Abstract:

This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.

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