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Modelling the Runtime of the Gaussian Computational Chemistry Application and Assessing the Impacts of Microarchitectural Variations

dc.contributor.authorAntony, Joseph
dc.contributor.authorRendell, Alistair
dc.contributor.authorYang, Rui
dc.contributor.authorTrucks, Gary W
dc.contributor.authorFrisch, Michael J
dc.coverage.spatialTsukuba Japan
dc.date.accessioned2015-12-10T22:16:50Z
dc.date.createdJune 1-3 2011
dc.date.issued2011
dc.date.updated2016-02-24T11:30:30Z
dc.description.abstractThis paper explores the use of a simple linear performance model, that determines execution time based instruction and cache miss counts, for describing the behaviour of two-electron integral evaluation algorithm in the Gaussian computational chemistry package. Four different microarchitecture platforms are considered with a total of seven individual microprocessors. Both Hartree-Fock and hybrid Hartree-Fock/Density Functional Theory electronic structure methods are assessed. In most cases the model is found to be accurate to within 3%. Least agreement is for an Athlon64 system (ranging from 1.8% to 6.5%) and a periodic boundary computation on an Opteron where errors of up to 6.8% are observed. These errors arise as the model does not account for the intricacies of out-of-order execution, on-chip write-back buffers and prefetch techniques that modern microprocessors implement. The parameters from the linear performance model are combined with instruction and cache miss counts obtained from functional cache simulation to predict the effect of cache modification on total execution time. Variations in level 1 and 2 linesize and level 2 total size are considered, we find there is some benefit if linesizes are increased (L1: 8%, L2: 4%). Increasing the level 2 cache size is also predicted to be beneficial, although the cache blocking approach already implemented in the Gaussian integral evaluation code was found to be working well.
dc.identifier.urihttp://hdl.handle.net/1885/51131
dc.publisherElsevier
dc.relation.ispartofseriesInternational Conference on Computational Science (ICCS 2011)
dc.sourceBlock-Entropy Analysis of Climate Data
dc.subjectKeywords: B3LYP; Cache simulation; ERI; Gaussians; Hardware performance counters; LPM; Performance Model; Computer simulation; Electronic structure; Errors; Gaussian distribution; Microprocessor chips; Computational chemistry B3LYP; Cache simulation; ERI; Gaussian; Hardware performance counters; HF; LPM; Performance model; PRISM
dc.titleModelling the Runtime of the Gaussian Computational Chemistry Application and Assessing the Impacts of Microarchitectural Variations
dc.typeConference paper
local.bibliographicCitation.startpage11
local.contributor.affiliationAntony, Joseph, College of Engineering and Computer Science, ANU
local.contributor.affiliationRendell, Alistair, College of Engineering and Computer Science, ANU
local.contributor.affiliationYang, Rui, University of Wollongong
local.contributor.affiliationTrucks, Gary W, Gaussian Inc
local.contributor.affiliationFrisch, Michael J, Gaussian Inc
local.contributor.authoruidAntony, Joseph, u2576015
local.contributor.authoruidRendell, Alistair, u9507815
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080399 - Computer Software not elsewhere classified
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4963866xPUB217
local.identifier.doi10.1016/j.procs.2011.04.030
local.identifier.scopusID2-s2.0-79958275437
local.identifier.thomsonID000299165200029
local.type.statusPublished Version

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