MPI for Python: Performance improvements and MPI-2 extensions

Dalcín, Lisandro; Paz, Rodrigo R.; Storti, Mario A.; D’Elı́a, Jorge

doi:10.1016/j.jpdc.2007.09.005

Cited by 293 publications

(183 citation statements)

References 12 publications

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“…We use MPI4PY (Dalcín et al 2008) to take advantage of multi-core processors. It is straightforward to calculate the luminosity in a distributed computation scheme since the necessary quantities are located independently in the fluid columns.…”

Section: A2 Numerical Implementationmentioning

confidence: 99%

Common Envelope Light Curves. I. Grid-code Module Calibration

Galaviz

Marco

Passy

et al. 2017

ApJS

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The common envelope (CE) binary interaction occurs when a star transfers mass onto a companion that cannot fully accrete it. The interaction can lead to a merger of the two objects or to a close binary. The CE interaction is the gateway of all evolved compact binaries, all stellar mergers,and likely many of the stellar transients witnessed to date. CE simulations are needed to understand this interaction and to interpret stars and binaries thought to be the byproduct of this stage. At this time, simulations are unable to reproduce the few observational data available and several ideas have been put forward to address their shortcomings. The need for more definitive simulation validation is pressingand is already being fulfilled by observations from time-domain surveys. In this article, we present an initial method and its implementation for post-processing grid-based CE simulations to produce the lightcurve so as to compare simulations with upcoming observations. Here we implemented a zeroth order method to calculate the light emitted from CE hydrodynamic simulations carried out with the 3D hydrodynamic code Enzo used in unigrid mode. The code implements an approach for the computation of luminosity in both optically thick and optically thin regimes and is tested using the first 135 days of the CE simulation of Passy et al., where a 0.8M e red giant branch star interacts with a 0.6M e companion. This code is used to highlight two large obstacles that need to be overcome before realistic light curves can be calculated. We explain the nature of these problems and the attempted solutions and approximations in full detail to enable the next step to be identified and implemented. We also discuss our simulation in relation to recent data of transients identified as CE interactions.

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Section: A2 Numerical Implementationmentioning

confidence: 99%

Common Envelope Light Curves. I. Grid-code Module Calibration

Galaviz

Marco

Passy

et al. 2017

ApJS

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“…Note that the current AmiRNA/WMD3 target search may contain few false negative/positive predicted amiRNA targets (Schwab et al, 2005), which can be minimized when new target search algorithms become available. Scripts written in Python were used for preprocessing, parallel processing (Dalcin et al, 2008), and postprocessing of the data. Large-scale designs were run on a high-performance cluster (2048 CPUs and 256 nodes; Triton Resource; San Diego Supercomputer Center; University of California San Diego).…”

Section: Analyses Of Protein Family Definitionsmentioning

confidence: 99%

A Genomic-Scale Artificial MicroRNA Library as a Tool to Investigate the Functionally Redundant Gene Space inArabidopsis

et al. 2013

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Traditional forward genetic screens are limited in the identification of homologous genes with overlapping functions. Here, we report the analyses and assembly of genome-wide protein family definitions that comprise the largest estimate for the potentially redundant gene space in Arabidopsis thaliana. On this basis, a computational design of genome-wide family-specific artificial microRNAs (amiRNAs) was performed using high-performance computing resources. The amiRNA designs are searchable online (http://phantomdb.ucsd.edu). A computationally derived library of 22,000 amiRNAs was synthesized in 10 sublibraries of 1505 to 4082 amiRNAs, each targeting defined functional protein classes. For example, 2964 amiRNAs target annotated DNA and RNA binding protein families and 1777 target transporter proteins, and another sublibrary targets proteins of unknown function. To evaluate the potential of an amiRNA-based screen, we tested 122 amiRNAs targeting transcription factor, protein kinase, and protein phosphatase families. Several amiRNA lines showed morphological phenotypes, either comparable to known phenotypes of single and double/triple mutants or caused by overexpression of microRNAs. Moreover, novel morphological and abscisic acid-insensitive seed germination mutants were identified for amiRNAs targeting zinc finger homeodomain transcription factors and mitogen-activated protein kinase kinase kinases, respectively. These resources provide an approach for genome-wide genetic screens of the functionally redundant gene space in Arabidopsis.

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“…All numerical simulations presented were performed within a programming environment built upon MPI for Python (Dalcín et al 2008), PETSc for Python, and PETSc-FEM (Sonzogni et al 2002). PETSc-FEM is a parallel multiphysics code primarily targeted to 2D and 3D finite elements computations on general unstructured grids.…”

Section: Softwarementioning

confidence: 99%

Modeling and high performance simulation of electrophoretic techniques in microfluidic chips

Kler

Berli

Guarnieri

2010

Microfluid Nanofluid

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Electrophoretic separations comprise a group of analytical techniques such as capillary zone electrophoresis, isoelectric focusing, isotachophoresis, and free flow electrophoresis. These techniques have been miniaturized in the last years and now represent one of the most important applications of the lab-on-a-chip technology. A 3D and time-dependent numerical model of electrophoresis on microfluidic devices is presented. The model is based on the set of equations that governs electrical phenomena, fluid dynamics, mass transport, and chemical reactions. The relationship between the buffer characteristics (ionic strength and pH) and surface potential of channel walls is taken into consideration. Numerical calculations were performed by using PETSc-FEM, in a Python environment, employing high performance parallel computing. The method includes a set of last generation preconditioners and solvers, especially addressed to 3D microfluidic problems, which significantly improve the numerical efficiency in comparison with typical commercial software for multiphysics. In this work, after discussing two validation examples, the numerical prototyping of a microfluidic chip for twodimensional electrophoresis is presented.

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MPI for Python: Performance improvements and MPI-2 extensions

Cited by 293 publications

References 12 publications

Common Envelope Light Curves. I. Grid-code Module Calibration

Common Envelope Light Curves. I. Grid-code Module Calibration

A Genomic-Scale Artificial MicroRNA Library as a Tool to Investigate the Functionally Redundant Gene Space inArabidopsis

Modeling and high performance simulation of electrophoretic techniques in microfluidic chips

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