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Report on subcontract from Lawrence Livermore National Lab, "Development of Large-Dimension Configuration-Interaction Shell-Model Code"
Author(s) -
Christopher R. Johnson
Publication year - 2012
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/1035605
Subject(s) - computer science , fortran , lanczos algorithm , lanczos resampling , national laboratory , hamiltonian (control theory) , dimension (graph theory) , computational science , parallel computing , programming language , physics , mathematics , mathematical optimization , combinatorics , eigenvalues and eigenvectors , engineering physics , quantum mechanics
The project period was devoted to several developments in the technical capabilities of the BIGSTIC large-dimension configuration-interaction shell-model code, written in Fortran 90. The specific computational goals for the project period were: (1) store Lanczos vectors on core in RAM to minimize I/O; (2) rewrite reorthogonalization with Lanczos vectors stored in core, consult with personnel at LLNL, LBL, ORNL, Iowa State University to maximize performance; (3) restrict creation of N-body jumps to those needed by an individual node; and (4) distribute 3-body interaction over many cores. Significant progress was made towards these goals, especially (1) and (2), although in the process they discovered intermediate tasks that had to be accomplished first. The achievements were as follows - I put into place structures and algorithms to facility fragmenting very large-dimension Lanczos intermediate vectors. Only by fragmenting the vectors can we carry out (1) and (2). In addition, I reorganized the action of the Hamiltonian matrix and created a new division of operations for MPI. Based upon earlier work, I made plans of a revised algorithm for distribution of work with MPI, with a particular eye towards breaking up the Lanczos vectors. I introduce a new derived type (opbundles) which collects the parameters for the Hamiltonian, and rewrote the application routines to use it. It has been validated and verified. I made progress towards revised MPI parallelization. Using the opbundles, I was able to compute a distribution of work over compute nodes, which should be very efficient. This new distribution is easier to derive and more efficient, in principle, than the old distribution. Furthermore, it should make applications with fragmented Lanczos vectors easier. Implementation is still in progress

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