LEADER 02927nam a22003017i 4500
006 m d
007 cr un
008 161208s2016 xx sm 000 0 eng d
a| CSt c| CSt d| UtOrBLW
a| Kokkila Schumacher, Sara I. L. ?| UNAUTHORIZED
a| Graphics processing unit accelerated tensor hypercontraction for high performance computing h| [electronic resource] : b| a reduction in computational cost of Ab initio methods / c| Sara I. L. Kokkila Schumacher.
a| 1 online resource.
a| Submitted to the Department of Chemistry.
a| Thesis (Ph.D.)--Stanford University, 2016.
a| Often, the computation of molecular properties requires an accurate description of the electronic wavefunction. Unfortunately, the accuracy and computational demands of a method are typically at odds with each other. In order to reduce the computational demands of ab initio methods, we have developed the tensor hypercontraction approach. In this work, we will show how the tensor hypercontraction approximation improves the efficiency of electronic structure methods while maintaining the accuracy of the underlying ab initio approach. This work focuses on the use of tensor hypercontraction in second-order Møller-Plesset perturbation theory (MP2), second-order approximate coupled cluster singles and doubles (CC2), and the extension of CC2 for excited state computations. Recently, the high performance computing industry has incorporated the use of graphics processing units (GPUs) for general purpose computing. GPUs are massively parallel architectures that are being used to accelerate computationally intensive approaches in a variety of fields, including quantum chemistry. I will show that the tensor hypercontraction methods are highly amenable to parallelization techniques and demonstrate a performance improvement for parallel tensor hypercontraction via parallelization across compute nodes and acceleration with GPUs. I will demonstrate that the use of parallel approaches allows us to extend the applicability of tensor hypercontraction CC2 and excited state computations to chemical system sizes that are challenging for canonical CC2.
a| Martinez, Todd J. q| (Todd Joseph), d| 1968- e| primary advisor. 4| ths =| ^A2683417
a| Markland, Thomas E., e| advisor. 4| ths =| ^A2683416
a| Ying, Lexing e| advisor. 4| ths =| ^A3113348
a| Stanford University. b| Department of Chemistry. =| ^A1328265
a| 21 22
z| Available to Stanford-affiliated users. u| http://purl.stanford.edu/gf611cf1441 x| SDR-PURL x| item
a| DATE CATALOGED b| 20170106
a| 3781 2016 K w| ALPHANUM c| 1 i| 36105223693651 l| UARCH-30 m| SPEC-COLL r| Y s| Y t| NONCIRC u| 12/9/2016
a| INTERNET RESOURCE w| ASIS c| 1 i| 11891209-2001 l| INTERNET m| SUL r| Y s| Y t| SUL u| 12/9/2016 x| E-THESIS