MADNESS provides a high-level environment for the solution of integral and differential equations in many dimensions using adaptive, fast methods with guaranteed precision based on multi-resolution analysis and novel separated representations. There are three main components to MADNESS. At the lowest level is a new petascale parallel programming environment that increases programmer productivity and code performance/scalability while maintaining backward compatibility with current programming tools such as MPI and Global Arrays. The numerical capabilities built upon the parallel tools provide a high-level environment for composing and solving numerical problems in many (1-6+) dimensions. Finally, built upon the numerical tools are new applications with initial focus upon chemistry, atomic and molecular physics, material science, and nuclear structure.
Please look in the wiki for more information and project activity.
Getting the sourceAnonymous, read-only source checkout:
svn checkout http://m-a-d-n-e-s-s.googlecode.com/svn/local/trunk m-a-d-n-e-s-s-read-onlyDevelopers, please see the wiki Subversion page for instructions.
Underneath the hoodIf you would like a glimpse at what's going on under the hood have a look at this call graph generated using the Google perftools. It nicely shows how work is funneled through the task-queue and how about 50% of the time is spent in the optimized matrix routines. The calculation computed the energy and gradient for di-nitrogen using the local density approximation on a two-core Thinkpad x61t.
FundingThe developers gratefully acknowledge the support of the Department of Energy, Office of Science, Office of Basic Energy Sciences and Office of Advanced Scientific Computing Research, under contract DE-AC05-00OR22725 with Oak Ridge National Laboratory.
The developers gratefully acknowledge the support of the National Science Foundation under grant 0509410 to the University of Tennessee in collaboration with The Ohio State University (P. Sadayappan). The MADNESS parallel runtime and parallel tree-algorithms include concepts and software developed under this project.
The developers gratefully acknowledge the support of the National Science Foundation under grant NSF OCI-0904972 to the University of Tennessee. The solid state physics and multiconfiguration SCF capabilities are being developed by this project.
The developers gratefully acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) under subcontract from Argonne National Laboratory as part of the High-Productivity Computer Systems (HPCS) language evaluation project.