Search results for "Computational Science"

showing 4 items of 124 documents

Potku – New analysis software for heavy ion elastic recoil detection analysis

2014

Time-of-flight elastic recoil detection (ToF-ERD) analysis software has been developed. The software combines a Python-language graphical front-end with a C code computing back-end in a user-friendly way. The software uses a list of coincident time-of- flight–energy (ToF–E) events as an input. The ToF calibration can be determined with a simple graphical procedure. The graphical interface allows the user to select different elements and isotopes from a ToF–E histogram and to convert the selections to individual elemental energy and depth profiles. The resulting sample composition can be presented as relative or absolute concentrations by integrating the depth profiles over user-defined rang…

ta113Nuclear and High Energy PhysicsIon beam analysista114business.industryComputer scienceEvent (computing)Reference data (financial markets)ion beam analysisERDelastic recoil detectionComputational scienceTOF-ERDElastic recoil detectionSoftwareCoincidentHistogramgraphical open source softwarebusinessInstrumentationGraphical user interfaceNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
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Visualization in the integrated SimPhoNy multiscale simulation framework

2018

Abstract We describe three distinct approaches to visualization for multiscale materials modelling research. These have been developed with the framework of the SimPhoNy FP7 EU-project, and complement each other in their requirements and possibilities. All have been integrated via wrappers to one or more of the simulation approaches within the SimPhoNy project. In this manuscript we describe and contrast their features. Together they cover visualization needs from electronic to macroscopic scales and are suited to simulations made on personal computers, workstations or advanced High Performance parallel computers. Examples as well as recommendations for future calculations are presented.

ta113Workstation010308 nuclear & particles physicsComputer scienceGeneral Physics and AstronomyElectronic charge density01 natural scienceselectronic charge densityComputational scienceVisualizationComplement (complexity)law.inventionatomisticHardware and Architecturelaw0103 physical sciencesCover (algebra)010306 general physicsvisualizationfluidComputer Physics Communications
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Lattice Boltzmann Simulations at Petascale on Multi-GPU Systems with Asynchronous Data Transfer and Strictly Enforced Memory Read Alignment

2015

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently general-purpose graphics processing units have become accessible as high-performance computing resources at large-scale. We report on implementing a lattice Boltzmann solver for multi-GPU systems that achieves 0.69 PFLOPS performance on 16384 GPUs. In addition to optimizing the data layout on the GPUs and eliminating the halo sites, we make use of the possibility to overlap data transfer between the host CPU and the device GPU with computing on the GPU. We simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on a large scale…

ta113ta114Computer scienceLattice Boltzmann methodsGPUParallel computingSolverLattice Boltzmannmemory alignmentComputational sciencePetascale computingAsynchronous communicationData structure alignmentGraphicsasynchronous communicationTitanHost (network)ComputingMethodologies_COMPUTERGRAPHICSData transmissionEuromicro international conference on parallel, distributed and network-based processing
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Scalable implementation of dependence clustering in Apache Spark

2017

This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed

ta113ta213Apache SparkComputer sciencedatasetsCorrelation clusteringdata miningcomputer.software_genrealgorithmsSpectral clusteringComputational sciencedependence clusteringData stream clusteringCURE data clustering algorithmScalabilitySpark (mathematics)algoritmitCanopy clustering algorithmData miningtiedonlouhintaCluster analysisclustering algorithmscomputerdata processingtietojenkäsittely
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