scholarly journals Speeding Up and Parallelizing the GARFIELD++

2018 ◽  
Vol 174 ◽  
pp. 06004
Author(s):  
A. Sheharyar ◽  
O. Bouhali ◽  
A. Castaneda
Keyword(s):  
Parallel Computing ◽  
Electric Field ◽  
Particle Detectors ◽  
Multiple Events ◽  
Enormous Amount ◽  
Speed Up ◽  
Search Operation ◽  
Computing Support ◽  
Simulation Time ◽  
Multiple Machines

Garfield++ is a toolkit for the simulation of particle detectors that use gas and semi-conductors as sensitive medium. It takes enormous amount of time to complete the simulation of complex scenarios such as those involving high detector voltages, gases with large gains, or electric field meshes with large number of elements. We observed that most of the simulation time is being consumed in finding the correct element in the electric field mesh. We optimized the element search operation and achieved significant boost in the speed up. In addition, We added the parallel computing support in the toolkit to simulate multiple events simultaneously over multiple machines. In this paper, we present our approach of speeding up the computations and benchmark results.

scholarly journals Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

2019 ◽  
Vol 28 ◽  
pp. 01031
Author(s):  
Rafal Szczepanski ◽  
Tomasz Tarczewski ◽  
Lech M. Grzesiak
Keyword(s):  
Parallel Computing ◽  
State Feedback ◽  
Computation Time ◽  
The Other ◽  
Speed Controller ◽  
Speed Up ◽  
Tuning Process ◽  
Auto Tuning ◽  
Reduce Computation Time ◽  
Simulation Time

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.

Hormones Nanofiltration in Carbon Nanotubes and Boron Nitride Nanotubes Using Uniform External Electric Field Through Molecular Dynamics

2021 ◽  
Vol 21 (11) ◽  
pp. 5499-5509
Author(s):  
Rosely Maria dos Santos Cavaleiro ◽  
Tiago da Silva Arouche ◽  
Phelipe Seiichi Martins Tanoue ◽  
Tais Souza Sá Pereira ◽  
Raul Nunes de Carvalho Junior ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Molecular Dynamics ◽  
Boron Nitride ◽  
Electric Field ◽  
Field Potential ◽  
Boron Nitride Nanotubes ◽  
Attractive Potential ◽  
Water Molecules ◽  
Computer Simulation Study ◽  
Simulation Time

Hormones are a dangerous group of molecules that can cause harm to humans. This study based on classical molecular dynamics proposes the nanofiltration of wastewater contaminated by hormones from a computer simulation study, in which the water and the hormone were filtered in two single-walled nanotube compositions. The calculations were carried out by changing the intensities of the electric field that acted as a force exerting pressure on the filtration along the nanotube, in the simulation time of 100 ps. The hormones studied were estrone, estradiol, estriol, progesterone, ethinylestradiol, diethylbestrol, and levonorgestrel in carbon nanotubes (CNTs) and boron nitride (BNNTs). The most efficient nanofiltrations were for fields with low intensities in the order of 10-8 au and 10-7 au. The studied nanotubes can be used in membranes for nanofiltration in water treatment plants due to the evanescent field potential caused by the action of the electric field inside. Our data showed that the action of EF in conjunction with the van der Walls forces of the nanotubes is sufficient to generate the attractive potential. Evaluating the transport of water molecules in CNTs and BNNTs, under the influence of the electric field, a sequence of simulations with the same boundary conditions was carried out, seeking to know the percentage of water molecules filtered in the nanotubes.

scholarly journals Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 22-30
Author(s):  
Erna Nurmawati ◽  
Robby Hasan Pangaribuan ◽  
Ibnu Santoso
Keyword(s):  
Parallel Computing ◽  
Missing Data ◽  
Data Processing ◽  
Em Algorithm ◽  
Parallel Computation ◽  
Incomplete Data ◽  
Parallel Programs ◽  
Missing Value ◽  
Speed Up ◽  
Compute Time

One way to deal with the presence of missing value or incomplete data is to impute the data using EM Algorithm. The need for large and fast data processing is necessary to implement parallel computing on EM algorithm serial program. In the parallel program architecture of EM Algorithm in this study, the controller is only related to the EM module whereas the EM module itself uses matrix and vector modules intensively. Parallelization is done by using OpenMP in EM modules which results in faster compute time on parallel programs than serial programs. Parallel computing with a thread of 4 (four) increases speed up, reduces compute time, and reduces efficiency when compared to parallel computing by the number of threads 2 (two).

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals A parallel computational framework for ultra-large-scale sequence clustering analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 380-388 ◽  
Author(s):  
Wei Zheng ◽  
Qi Mao ◽  
Robert J Genco ◽  
Jean Wactawski-Wende ◽  
Michael Buck ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Computational Framework ◽  
Speed Up ◽  
Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

Nano-Indentation Simulation Study Based on Parallel Computing

2012 ◽  
Vol 500 ◽  
pp. 696-701
Author(s):  
Ying Zhu ◽  
Sen Song ◽  
Ling Ling Xie ◽  
Shun He Qi ◽  
Qian Qian Liu
Keyword(s):  
Molecular Dynamics ◽  
Parallel Computing ◽  
Molecular Dynamics Simulation ◽  
Large Scale ◽  
Dynamics Simulation ◽  
Parallel Computer ◽  
Nano Indentation ◽  
Simulation Results ◽  
The Impact ◽  
Simulation Time

This method of parallel computing into nanoindentation molecular dynamics simulation (MDS), the author uses a nine-node parallel computer and takes the single crystal aluminum as the experimental example, to implement the large-scale process simulation of nanoindentation. Compared the simulation results with experimental results is to verify the reliability of the simulation. The method improves the computational efficiency and shortens the simulation time and the expansion of scale simulation can significantly reduce the impact of boundary conditions, effectively improve the accuracy of the molecular dynamics simulation of nanoindentation.

scholarly journals Compact representation of generalized molecular polarizabilities and efficient calculation of polarization energy in an arbitrary electric field

2021 ◽  
Author(s):  
Krzysztof Wolinski ◽  
Peter Pulay
Keyword(s):  
Electric Field ◽  
Compact Representation ◽  
Efficient Calculation ◽  
Density Response Function ◽  
Molecular Charge ◽  
Order Of Magnitude ◽  
Speed Up ◽  
Molecular Polarizabilities ◽  
Polarizability Matrix ◽  
Natural Polarization

Generalized polarizabilities and the molecular charge distribution can describe the response of a molecule in an arbitrary static electric field up to second order. Depending on the expansion functions used to describe the perturbing potential, the generalized polarizability matrix can have rather large dimension (~1000). This matrix is the discretized version of the density response function or electronic susceptibility. Diagonalizing and truncating it can lead to significant (over an order of magnitude) speed-up in simulations. We have analyzed the convergence behavior of the generalized polarizability using a plane wave basis for the potential. The eigenfunctions of the generalized polarizability matrix are the natural polarization potentials. They are potentially useful to construct efficient polarizability models for molecules.

scholarly journals A Parallel Processing Approach to Dynamic Simulation of Ethylbenzene Process

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1386
Author(s):  
Junkai Zhang ◽  
Zhongqi Liu ◽  
Zengzhi Du ◽  
Jianhong Wang
Keyword(s):  
Parallel Computing ◽  
Dynamic Simulation ◽  
Process Simulation ◽  
Chemical Process ◽  
Process Model ◽  
Chemical Processes ◽  
Data Driven ◽  
Computer Performance ◽  
Speed Up ◽  
General Dynamic

Parallel computing has been developed for many years in chemical process simulation. However, existing research on parallel computing in dynamic simulation cannot take full advantage of computer performance. More and more applications of data-driven methods and increasing complexity in chemical processes need faster dynamic simulators. In this research, we discuss the upper limit of speed-up for dynamic simulation of the chemical process. Then we design a parallel program considering the process model solving sequence and rewrite the General dynamic simulation & optimization system (DSO) with two levels of parallelism, multithreading parallelism and vectorized parallelism. The dependency between subtasks and the characteristic of the hottest subroutines are analyzed. Finally, the accelerating effect of the parallel simulator is tested based on a 500 kt·a−1 ethylbenzene process simulation. A 5-hour process simulation shows that the highest speed-up ratio to the original program is 261%, and the simulation finished in 70.98 s wall clock time.

scholarly journals Reducing molecular simulation time for AFM images based on super-resolution methods

2021 ◽  
Vol 12 ◽  
pp. 775-785
Author(s):  
Zhipeng Dou ◽  
Jianqiang Qian ◽  
Yingzi Li ◽  
Rui Lin ◽  
Jianhai Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Simulation ◽  
Super Resolution ◽  
Training Data ◽  
Reconstruction Algorithms ◽  
Force Microscopy ◽  
Nanoscale Imaging ◽  
Speed Up ◽  
Energy Maps ◽  
Simulation Time

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

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