Synchrotron Light-Source Data Analysis PDF
Preview Synchrotron Light-Source Data Analysis
SynchrotronLight-sourceDataAnalysisthrough Massively-parallelGPUComputing AbhinavSarje ComputationalResearchDivision LawrenceBerkeleyNationalLaboratory GPUTechnologyConference2013 Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions SynchrotronLight-Sources • Electronaccelerator togenerate high-intensity electromagnetic radiation. • Radiationinform ofhigh-intensity beams,usedfor experimentsat beamlines. AdvancedLightSource@BerkeleyLab SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions High-energyX-rayScattering • X-rayscatteringtomeasurestructuralpropertiesofmaterials,and • characterizemacromoleculesandnano-particlesystemsatmicroand nano-scales. • probingtheelectronicstructureofmatter, • semiconductors, • 3D-biologicalimaging, • proteincrystallography, • chemicalreactiondynamics, • biologicalprocessdynamics, • optics, • ..andsoon. • Broadvarietyofapplications.E.g.: • Materials:Designofenergyefficientdeviceslikesolarcells,high-density storagemedia • Medicine:Designofsyntheticenzymes,drugsandbio-membranes. SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions High-energyX-rayScattering graphic:courtesyofA.Meyer,www.gisaxs.de Examples: • Small-angleX-rayScattering(SAXS) • GrazingIncidenceSAXS(GISAXS). SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions Outline 1 MotivationsandtheneedofHPC. 2 Computationalproblemsinstructureprediction. • Scatteringpatternsimulations. • Inversemodeling/fitting. 3 GISAXSsimulations. 4 HipGISAXS:anHPCsolution. • Implementationandoptimizations. • Performanceanalysis. 5 Conclusionsandendingnotes. SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions ComputationalProblemsinStructurePrediction • Anexampleworkflow: Start Initialmodel SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions ComputationalProblemsinStructurePrediction • Anexampleworkflow: Start compute pattern Initialmodel SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions ComputationalProblemsinStructurePrediction • Anexampleworkflow: Start compute pattern Initialmodel matchwith experimentaldata SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions ComputationalProblemsinStructurePrediction • Anexampleworkflow: Start compute pattern Initialmodel matchwith experimentaldata tunemodel parameters SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab Introduction Motivation GISAXS Solution GPUClusters Experiments&Performance Conclusions ComputationalProblemsinStructurePrediction • Anexampleworkflow: Start compute pattern Initialmodel matchwith experimentaldata tunemodel parameters SynchrotronLight-sourceDataAnalysisthroughMassively-parallelGPUComputing AbhinavSarje@BerkeleyLab
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