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High performance computing in Python

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High performance computing in Python
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57
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CC Attribution 3.0 Germany:
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Production PlaceWageningen

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Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn) What are the possibilities to improve the performance of computation in Python? This tutorial shows how to performe Numpy operations using multicore processing, how to accelerate python functions using Numba, how to calculate fast numerical expression using NumExpr, how to use the TilingProcessing to distribute raster operations in multiple cores.
Keywords
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Process (computing)ImplementationOpen sourcePoint (geometry)Library (computing)Core dumpRootWeightMetropolitan area networkOperator (mathematics)Electronic signatureEndliche ModelltheorieParameter (computer programming)CASE <Informatik>Multiplication signComputer animationSource code
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Operator (mathematics)Medical imagingFunction (mathematics)Range (statistics)outputData structureShape (magazine)Dimensional analysisProcess (computing)Patch (Unix)PixelCompilerMultiplication signJust-in-Time-CompilerSpacetimeSingle-precision floating-point formatResultantComputer configurationMedianNumberLine (geometry)Three-dimensional spacePoint (geometry)HypermediaAxiom of choiceOrder (biology)Web 2.0WorkloadRootPhysical lawBit rateRight angleGroup actionCategory of beingCoroutineState of matterMathematical optimizationInformationComputer animation
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