15 papers · ranked by Valyu relevance
Gregor Mönke, Tim Schäfer, Mohsen Parto-Dezfouli, Diljit Singh Kajal + 3 more
'Stefan Fürtinger' 'Joscha Tapani Schmiedt' 'Pascal Fries'] We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g., time-lock…
Vladislav Skorpil, Vaclav Oujezsky, Arcangelo Castiglione, Gianni D’Angelo
'Gianni D’Angelo'] This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are…
Honggang Zhao, Benjamin Beck, Adam Fuller, Eric Peatman + 1 more
'Roberto Fritsche-Neto'] The software programs STRUCTURE and NEWHYBRIDS are widely used population genetic programs useful in addressing questions related to genetic structure, admixture, and hybridization. These programs usually require a large number of independent runs with many iterations to provide robust data for…
Fabian Woller, Lis Arend, Christian Fuchsberger, Markus List + 1 more
To demonstrate the practical applicability of NApy on a real-world dataset, all statistical tests were performed on the CHRIS study data both without any parallelization (single thread) and with heavy parallelization (64 threads). For correlations, pandas was used as comparison as it was the fastest compared to other…
Zeyi Zhang, Carlos Mora Perez, Patrick Kwon, Martin Head‐Gordon + 1 more
PARSEC.py is a Python-based real-space Kohn-Sham density functional theory (real-space KS-DFT) framework designed to provide a user- and developer-friendly platform for first-principles electronic-structure simulations. Discretization on real-space grids eliminates basis-set approximations, while enabling systematic…
Richard Gast, Daniel Rose, Christoph Salomon, Harald E. Möller + 3 more
'Nikolaus Weiskopf' 'Thomas R. Knösche' 'William W Lytton'] In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical…
Julia L Turner, Scott T Kelley, James S Otto, Faramarz Valafar + 1 more
'Andrew J Bohonak'] Background The Isolation by Distance Web Service (IBDWS) is a user-friendly web interface for analyzing patterns of isolation by distance in population genetic data. IBDWS enables researchers to perform a variety of statistical tests such as Mantel tests and reduced major axis regression (RMA), and…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw + 4 more
'Lutz Brusch' 'Jan Hasenauer' 'Yannik Schälte' 'Abel C.H. Chen'] Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow…
SPT Krishnan, Sim Sze Liang, Bharadwaj Veeravalli
Background RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence…
Marco D. Visser, Sean M. McMahon, Cory Merow, Philip M. Dixon + 3 more
Parallel computing divides calculations into smaller problems and solves these simultaneously, using multiple computing elements (hereafter “workers”). In the biological sciences, many computationally intensive problems are “embarrassingly parallel” , where almost all calculations can be completed in parallel. Common…
Patrizio Dazzi
Embarrassingly parallel problems are characterised by a very small amount of information to be exchanged among the parts they are split in, during their parallel execution. As a consequence they do not require sophisticated, low-latency, high-bandwidth interconnection networks but can be efficiently computed in…
Francois Besnier, Kevin A. Glover, Maria Anisimova
This software package provides an R-based framework to make use of multi-core computers when running analyses in the population genetics program STRUCTURE. It is especially addressed to those users of STRUCTURE dealing with numerous and repeated data analyses, and who could take advantage of an efficient script to…
Gonzalo Vera, Ritsert C Jansen, Remo L Suppi
Background R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example…