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Gustafsson, Magnus
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GoogleGoogle Scholar$(function(){PrimeFaces.cw('Chart','widget_formSmash_j_idt1230_0_downloads',{id:'formSmash:j_idt1230:0:downloads',type:'bar',responsive:true,data:[[6,24,10,11,7,12,18,12,18,9]],title:"Downloads of File (FULLTEXT01)",axes:{xaxis: {label:"",renderer:$.jqplot.CategoryAxisRenderer,tickOptions:{angle:-90}},yaxis: {label:"",min:0,max:30,renderer:$.jqplot.LinearAxisRenderer,tickOptions:{angle:0}}},series:[{label:'diva2:505808'}],ticks:["Aug -23","Sep -23","Oct -23","Nov -23","Dec -23","Jan -24","Feb -24","Mar -24","Apr -24","May -24"],orientation:"vertical",barMargin:3,datatip:true,datatipFormat:"<span style=\"display:none;\">%2$d</span><span>%2$d</span>"},'charts');}); Total: 911 downloads$(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_j_idt1236",{id:"formSmash:j_idt1236",widgetVar:"widget_formSmash_j_idt1236",target:"formSmash:downloadLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade"});}); findCitings = function() {PrimeFaces.ab({s:"formSmash:j_idt1240",f:"formSmash",u:"formSmash:citings",pa:arguments[0]});};$(function() {findCitings();}); $(function(){PrimeFaces.cw('Chart','widget_formSmash_visits',{id:'formSmash:visits',type:'bar',responsive:true,data:[[4,1,4,5,4,1,2,3,6,3]],title:"Visits for this publication",axes:{xaxis: {label:"",renderer:$.jqplot.CategoryAxisRenderer,tickOptions:{angle:-90}},yaxis: {label:"",min:0,max:10,renderer:$.jqplot.LinearAxisRenderer,tickOptions:{angle:0}}},series:[{label:'diva2:505808'}],ticks:["Jun -23","Sep -23","Oct -23","Nov -23","Dec -23","Jan -24","Feb -24","Mar -24","Apr -24","May -24"],orientation:"vertical",barMargin:3,datatip:true,datatipFormat:"<span style=\"display:none;\">%2$d</span><span>%2$d</span>"},'charts');}); Total: 869 hits
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Towards an adaptive solver for high-dimensional PDE problems on clusters of multicore processorsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2012 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala University, 2012.
##### Series

Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2012-003
##### National Category

Computer Sciences Computational Mathematics
##### Research subject

Scientific Computing
##### Identifiers

URN: urn:nbn:se:uu:diva-169259OAI: oai:DiVA.org:uu-169259DiVA, id: diva2:505808
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##### Projects

eSSENCEUPMARCAvailable from: 2012-03-09 Created: 2012-02-25 Last updated: 2019-02-25Bibliographically approved
##### List of papers

Accurate numerical simulation of time-dependent phenomena in many spatial dimensions is a challenging computational task apparent in a vast range of application areas, for instance quantum dynamics, financial mathematics, systems biology and plasma physics. Particularly problematic is that the number of unknowns in the governing equations (the number of grid points) grows exponentially with the number of spatial dimensions introduced, often referred to as the curse of dimensionality. This limits the range of problems that we can solve, since the computational effort and requirements on memory storage directly depend on the number of unknowns for which to solve the equations.

In order to push the limit of tractable problems, we are developing an implementation framework, HAParaNDA, for high-dimensional PDE-problems. By using high-order accurate schemes and adaptive mesh refinement (AMR) in space, we aim at reducing the number of grid points used in the discretization, thereby enabling the solution of larger and higher-dimensional problems. Within the framework, we use structured grids for spatial discretization and a block-decomposition of the spatial domain for parallelization and load balancing. For integration in time, we use exponential integration, although the framework allows the flexibility of other integrators to be implemented as well. Exponential integrators using the Lanzcos or the Arnoldi algorithm has proven a succesful and efficient approach for large problems. Using a truncation of the Magnus expansion, we can attain high levels of accuracy in the solution.

As an example application, we have implemented a solver for the time-dependent Schrödinger equation using this framework. We provide scaling results for small and medium sized clusters of multicore nodes, and show that the solver fulfills the expected rate of convergence.

1. An implementation framework for solving high-dimensional PDEs on massively parallel computers$(function(){PrimeFaces.cw("OverlayPanel","overlay359735",{id:"formSmash:j_idt583:0:j_idt587",widgetVar:"overlay359735",target:"formSmash:j_idt583:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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