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A Python Interface to Trilinos/Tpetra for High-Level Access to HPC Solvers

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-FG02-12ER90218
Agency Tracking Number: 98645
Amount: $149,575.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 02 b
Solicitation Number: DE-FOA-0000577
Timeline
Solicitation Year: 2012
Award Year: 2012
Award Start Date (Proposal Award Date): 2012-02-20
Award End Date (Contract End Date): 2012-11-19
Small Business Information
515 Congress Avenue Suite 2100
Austin, TX 78701-3555
United States
DUNS: 129923913
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Travis Oliphant
 Dr.
 (512) 536-1057
 oliphant@enthought.com
Business Contact
 Jodi Havranek
Title: Ms.
Phone: (512) 536-1057
Email: jhavranek@enthought.com
Research Institution
 Stub
Abstract

For many classes of programming problems that require parallel libraries, years of experience are required to effectively use those libraries, and frequently they are difficult to use, requiring complicated programming interfaces. The large time investment and limited usability prohibit typical domain specialists, who have limited programming expertise, from creating parallel codes and benefiting from parallel resources. Programs that can make this time investment typically have huge resources at their disposal, such as the Advanced Simulation & amp; Computing (ASC) campaign, or the Scientific Discovery through Advanced Computing (SciDAC) program. The motivation for industry users to use parallel libraries will only grow with time, as multi-core systems are becoming more widely available on commodity desktop and laptop systems, and it is not far off before hundred-core desktops and laptops are common. We propose to design a high-level interface to the Trilinos Tpetra parallel linear algebra library in the expressive and user-friendly Python language. This interface will make parallel linear algebra (1) easier to use via a simplified user interface, (2) more intuitive through features such as advanced indexing, and (3) more useful by enabling access to it from the already extensive Python scientific software stack. Commercial applications and other benefits: The project will result in better utilization of existing HPC resources and will facilitate a decrease in the total time to market from concept to implementation, improving the competitiveness of US industries, universities and defense organizations. The benefit to the public will be observed in diverse applications that use parallel linear algebra libraries for simulation and modeling: product design and manufacturing, pharmaceuticals, thermal management, network and data transmission design, medical imaging, operational logistics, environmental project planning, and risk and performance assessment, to name some examples.

* Information listed above is at the time of submission. *

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