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High Performance Computing Code (HPC) Modernization

Langley Research Center (LARC) - Internship
This project will support the NASA Langley Incubator, the goal of which is to identify, leverage, and integrate high performance computing, computational methods and capabilities, and data analytics/machine intelligence for scientific innovation. Students will have the opportunity to work with engineers, researchers, computer scientists, and technologists in the areas of aerospace systems design, computational materials, additive manufacturing, and vehicle flight prediction. Students will help with code conversion/migration for many-core architectures, evaluate and implement strategies for applying high performance computing to modeling and simulation problems and evaluate integration methodologies of HPC into laboratory environments. Students will evaluate and review use cases to demonstrate the capability on NASA projects.
Internship Facts
Session(s): Fall 2017
Available slots: 2
Academic Level(s): College - Junior
College - Senior
Pursuing Masters
Pursuing Doctorate
Pursuing Post Doctorate
Academic Discipline(s): Engineering - Computer Eng
Engineering - Electrical Eng.
Engineering - Mechanical Eng.
Mathematics - Applied Mathematics
Technology - Systems Eng./Design
Technology - Comp Science
NASA Center/Facility Name: Langley Research Center (LARC)
Building Number/Name & Room Number B1268 / 2081
Work Environment Office Setting
Expected outcome: Students will have the opportunity to engage in focused pilot demonstrations and ongoing research and development activities with the possibility of publication. A final report or poster presentation will be required.
Student's special skills: Students should have some experience with high performance computing, or parallel programming languages and tools. Candidates also require a working knowledge of and programming efficiency with C/C++ and/or Fortran and Linux operating systems. A working knowledge of and ability to apply parallel programming technologies (e.g., MPI, OpenMP, OpenACC, etc.) and code optimization is highly recommended, as is an understanding of many-core architectures (e.g., GPUs, Xeon Phi, etc.). Candidates should be motivated to work both independently and within team environments.
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