#use wml::std::lang #use wml::fmt::isolatin #include <menu-item.wml> #include <bibstyle.wml> #include <banner.wml> title="Research" path="research" <br> <center> <table border=0 cellspacing=0 cellpadding=5 width="98%"> <tr><td> <frame title="Documents"> <table border=0 cellspacing=0 cellpadding=10 width="100%"> <tr valign=top><td valign=top width="100%"> <ul> <li> a list of my <a href="publications.html">publications</a> (journals, conference's articles and freely available research reports) <li> some material from the <a href="talks.html">talks</a> I have given </ul> </td></tr></table> </frame> <frame title="Out-of-core solution for sparse direct methods"> <table border=0 cellspacing=0 cellpadding=5 width="98%"> <tr valign=top><td valign=top width="50%"> <p align="justify"> <h3>Motivations and Principles</h3> <table border="0" cellspacing="0" cellpadding="5" width="100%" summary=""> <tr><td> The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. The out-of-core approach consists in extending the core memory by disks. </td><td> <a href="Img/tomography_large.jpg"><img align=top border=0 src="Img/tomography_large.jpg" alt="" width="200" height="200"></a> </td></tr> </table> <h3>Results</h3> We have developed a prototype of an out-of-core extension to a parallel multifrontal solver (<a href="http://graal.ens-lyon.fr/MUMPS/">MUMPS</a>). We show that, by storing the factors to disk, larger problems can be solved on limited-memory machines with reasonable performance (see <a href="publications.html#agul:06">article in EuroPar'06</a>). We have illustrated the impact of low-level IO mechanisms on the behaviour of our parallel out-of-core factorization. Then have used simulations to discuss how our algorithms can be modified to solve much larger problems at the cost of increasing the volume of disk access (see <a href="publications.html#agul:06b">article in RenPar'06</a>). </p> </td></tr></table> </frame> </td></tr></table> </center>