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dc.contributor.authorMandli, Kyle
dc.contributor.authorAlghamdi, Amal
dc.contributor.authorAhmadia, Aron
dc.contributor.authorKetcheson, David I.
dc.contributor.authorScullin, William
dc.date.accessioned2014-11-04T08:51:25Z
dc.date.available2014-11-04T08:51:25Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10754/333644
dc.description.abstractComputational scientists seek to provide efficient, easy-to-use tools and frameworks that enable application scientists within a specific discipline to build and/or apply numerical models with up-to-date computing technologies that can be executed on all available computing systems. Although many tools could be useful for groups beyond a specific application, it is often difficult and time consuming to combine existing software, or to adapt it for a more general purpose. Python enables a high-level approach where a general framework can be supplemented with tools written for different fields and in different languages. This is particularly important when a large number of tools are necessary, as is the case for high performance scientific codes. This motivated our development of PetClaw, a scalable distributed-memory solver for time-dependent nonlinear wave propagation, as a case-study for how Python can be used as a highlevel framework leveraging a multitude of codes, efficient both in the reuse of code and programmer productivity. We present scaling results for computations on up to four racks of Shaheen, an IBM BlueGene/P supercomputer at King Abdullah University of Science and Technology. One particularly important issue that PetClaw has faced is the overhead associated with dynamic loading leading to catastrophic scaling. We use the walla library to solve the issue which does so by supplanting high-cost filesystem calls with MPI operations at a low enough level that developers may avoid any changes to their codes.
dc.language.isoen
dc.publisherTHE 10th PYTHON IN SCIENCE CONF. (SCIPY 2011)
dc.relation.urlhttp://conference.scipy.org/proceedings/scipy2011/pdfs/mandli_etal.pdf
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.subjectparallel
dc.subjectfinite volume
dc.subjectscaling
dc.subjectnonlinear waves
dc.subjectPyClaw
dc.subjectPetClaw
dc.subjectWalla
dc.titleUsing Python to Construct a Scalable Parallel Nonlinear Wave Solver
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentNumerical Mathematics Group
dc.identifier.journalProceedings of the 10th PYTHON IN SCIENCE CONF. (SCIPY 2011)
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of Washington
dc.contributor.institutionArgonne National Labs
refterms.dateFOA2018-06-14T07:15:25Z


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