Future Directions for NSF Advanced Computing Infrastructure - PowerPoint Presentation

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Future Directions for NSF Advanced Computing Infrastructure to support US Science in 2017-2020

ACCI meeting April 2, 2014Jon EisenbergDirector, CSTB

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Credit: National Academy of Sciences Slide3

National Academies today

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charge to committee

A study committee will examine anticipated priorities and associated tradeoffs

for advanced computing in support of NSF-sponsored science and engineering research.   The committee will consider:The

contribution of high end computing to U.S. leadership and competiveness

in basic science and engineering and

the role that NSF should play in sustaining this leadership

Expected

future national-scale computing needs

: high-end requirements, those arising from the full range of basic science and engineering research supported by NSF, as well as the computing infrastructure needed to support advances in both modeling, simulation and data analysis

Complementarities

and tradeoffs

that arise among investments in supporting advanced computing ecosystems; software, data, communications

The range of operational models for delivering computational infrastructure, for basic science and engineering research, and the role of NSF support in these various models Expected technical challenges to affordably delivering the capabilities needed for world-leading scientific and engineering research

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reports

interim report (Summer 2014) to identify key issues and discuss potential options.  It might contain preliminary findings and early

recommendationsfinal report (2015) to include a framework for future decision-making about

NSF’s

advanced computing strategy and

programs

how

to prioritize needs and investments and how to balance competing demands for

cyberinfrastructure

investments

approach: identifying

issues, explicating options, and articulating tradeoffs and general recommendationsNB: no recommendations concerning the level of federal funding for computing infrastructure5Slide6

committee “spec”

mix of science/engineering users of advanced computing, computational scientists

, and experts in the underlying computing technologiesboth compute- and

data-intensive science

experience with

NSF, DOE, and DOD programs and facilities

e

xperience with

management of ACI facilities

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roader

science policy context

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nstitutional and individual diversityNAS and NAE members6Slide7

committee

William Gropp, UIUC (co-chair)Robert

Harrison, Stony Brook/Brookhaven (co-chair)Mark R. Abbott, Oregon State

David

Arnett, Univ. of Arizona

Robert

Grossman, Univ. of Chicago/Open Data Group

Peter

Kogge

, Notre Dame

Padma

Raghavan

, Penn. State

Daniel A. Reed, Univ. of IowaValerie Taylor, Texas A&MKatherine Yelick, UC Berkeley/LBNL

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(draft) questions to inform interim report

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cience needs/opportunities

What are some of the open problems in your field that require large scale simulation to solve? Which might lead to fundamental or foundational advances? Why are these problems not being solved today?

What are some of the open problems in your field that require data intensive computing, such as large scale data analytics and data mining? Why are these problems not being solved today?

Are there plans or roadmaps that characterize future computing needs in your field?

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dvanced computing capabilities, facilities, requirements

What forms of computing are used in your field? E.g., how does your field make use of laptop/desktops, research group clusters, department or campus commodity cluster systems, mid-to large-scale, shared capacity systems such as XSEDE, leadership-class capability systems such as Blue Waters (NSF) or Mira (DOE), or commercial cloud services such as Amazon EC2? How would you characterize the importance of access to each type--required, desirable, or unnecessary? How might these needs change in the future, and why?

With computer hardware and software evolving more rapidly than in the recent past, what impacts do you see for your field? For example, what role will new hardware such accelerators (GPUs or Intel Xeon Phi), FPGAs, new memory systems, or new I/O systems play? Are there barriers to their adoption, such as challenges making necessary modifications to software?

What software does your field depend on? Who develops and maintains this code, and how is this work supported?

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hallenges and suggestions

What are the biggest challenges that your field faces in using computation? Consider access to systems with sufficient capability and capacity; productivity of environments; algorithms; workforce; stability of software and hardware; and the ability to use systems efficiently, including parallelism and scalability.

What investments would have the greatest positive impact on your research field? For example, this could be more computer systems to increase access, different kinds of systems with a different balance of capability, support for community software, development of new algorithms, or a workforce with better training in computational science.

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inputs

web conference briefings for April/Maywritten commentsreport reviewf

urther briefings and discussion to develop final reportsuggestions for questions, individuals, institutions, programs, and research areas to consider?

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feedback/suggestions

Please send suggestions for people to hear from, topics, to consider, responses to draft questions to: jeisenbe@nas.eduFor more on project, see

www.cstb.org

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