1. National Strategic Computing Initiative
Irene Qualters
Division Director, ACI
Computer & Information Science & Engineering
October 16, 2015

2. National Strategic Computing Initiative Executive Order Signed July 29, 2015
“Whole of government” approach
Public/private partnership with industry
and academia
Strategic
Leverage beyond individual programs
Long time horizon (decade or more)
Computing
HPC as advanced, capable computing technology
Multiple styles of computing and all necessary infrastructure
Scope includes everything necessary for a fully integrated capability
Initiative
Above baseline effort
Link and lift efforts
Enhance U.S. strategic advantage in HPC for
economic competitiveness and scientific discovery

3. NSCI Executive Order calls on NSF to play a leadership role
Scientific discovery advances
The broader HPC ecosystem for scientific discovery
Workforce development
NSF is in a leadership position to advance the science and engineering foundations for HPC over a two-decade time horizon, and to reap the benefits of applying HPC advances to all science and engineering domains.  
The NSCI calls on NSF to play a central role in…
We are co-leading NSCI with DOD and DOE
Co-lead with DOD and DOE

4. The Government’s Role in NSCI
DOD + DOE
Capable exascale program
Analytic computing to support missions: science and national security
NSF
Scientific discovery
Broader HPC ecosystem
Workforce Development
IARPA + NIST
Future computing technologies
NASA, FBI, NIH, DHS, NOAA
Deployment within their mission contexts
The NSCI is formulated as a “whole of government” activity

5. NSCI Objectives
Accelerate delivery of a capable exascale computing system (hardware, software) to deliver approximately 100X the performance of current 10PF systems across a range of applications reflecting government needs
Increase coherence between technology base used for modeling and simulation and that used for data analytic computing.
Establish, over the next 15 years, a viable path forward for future HPC systems in the post Moore’s Law …
Increase the capacity and capability of an enduring national HPC ecosystem, employing a holistic approach … networking, workflow, downward scaling, foundational algorithms and software, and workforce development.
Develop an enduring public-private partnership to assure that the benefits .. are transferred to the U.S. commercial, government, and academic sectors

6. Next Steps for NSCI Executive Council established Implementation Plan
Co-chaired by the Directors of OSTP and of OMB
Membership representing participating agencies as designated by the Director of OSTP
Implementation Plan
To be established within 90 days (of July 29, 2015)
And annually thereafter for 5 years
NIH/DOE/NSF Request For Information (RFI)
“Science Drivers Requiring Capable Exascale High Performance Computing”
response deadline has been extended to November 13
White House National Strategic Computing Initiative Workshop (Oct.20-21)

7. 3. Establish, over the next 15 years, a viable path forward for future HPC systems in the post Moore’s Law era
Happening now
Multi-core and many-core processors
Domain-specific integrated circuits
Energy-aware computing
Hierarchical memories
High-speed Interconnects
Longer term
New materials (e.g., carbon nano-tubes, graphene-based devices)
Usable parallelism, concurrency, and scalability
Non-charge transfer devices (e.g., electron spin)
Resiliency at scale
Decreased power consumption
Bio, nano, and quantum devices
Architectures that reduce data movement
NSF Role: Support foundational research
(leadership by ENG, CISE, MPS, and BIO)

8. 2. Increase coherence between technology base used for modeling and simulation and that used for data analytic computing
Modeling and Simulation
Multi-scale
Multi-physics
Multi-resolution
Multidisciplinary
Coupled models
Data Science
Data Assimilation
Visualization
Image Analysis
Data Compression
Data Analytics
Machine Learning
Time
NSF Role: Support foundational research and research infrastructure within and across all disciplines (across all NSF directorates)

9. Data Science : Emerging …..Inherently multidisciplinary
Engineering Practice
Theoretical Foundation
Technical Skills
2010 Drew Conway’s Venn Diagram
Mathematical and CS Theory
Software Engineering
Domain Knowledge
For better or worse, data is a commodity traded electronically; therefore, in order to be in this market you need to speak hacker. This, however, does not require a background in computer science—in fact—many of the most impressive hackers I have met never took a single CS course. Being able to manipulate text files at the command-line, understanding vectorized operations, thinking algorithmically; these are the hacking skills that make for a successful data hacker.
Once you have acquired and cleaned the data, the next step is to actually extract insight from it. In order to do this, you need to apply appropriate math and statistics methods, which requires at least a baseline familiarity with these tools. This is not to say that a PhD in statistics in required to be a competent data scientist, but it does require knowing what an ordinary least squares regression is and how to interpret it.
In the third critical piece—substance—is where my thoughts on data science diverge from most of what has already been written on the topic. To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.
Finally, a word on the hacking skills plus substantive expertise danger zone. This is where I place people who, "know enough to be dangerous," and is the most problematic area of the diagram. In this area people who are perfectly capable of extracting and structuring data, likely related to a field they know quite a bit about, and probably even know enough R to run a linear regression and report the coefficients; but they lack any understanding of what those coefficients mean. It is from this part of the diagram that the phrase "lies, damned lies, and statistics" emanates, because either through ignorance or malice this overlap of skills gives people the ability to create what appears to be a legitimate analysis without any understanding of how they got there or what they have created. Fortunately, it requires near willful ignorance to acquire hacking skills and substantive expertise without also learning some math and statistics along the way. As such, the danger zone is sparsely populated, however, it does not take many to produce a lot of damage.
Domain Knowledge

10. Aspirations for Convergence
State of the Art
“Big Data”
Data Analytics
Data Intensity
Large Scale
Data Driven
Modeling
And Simulation
Goal of Initiative
State of the Art
High-Performance
Modeling
And Simulation
Computational Intensity

11. 2. Increase synergy between technology base used for modeling and simulation and that used for data analytic computing 4. Increase the capacity and capability of an enduring national HPC ecosystem, employing a holistic approach … networking, workflow, downward scaling, foundational algorithms and software, and workforce development.
NSF Role:
Research Priorities:
Computationally and Data Intensive Science and Engineering Frontiers
Discovery-motivated computational investments:
Cyberinfrastructure: people, software, technology
Collaborations likely: interagency, public sector, industry, international
Re-use, agility, sustainability
Directorates: All

12. The conduct of science is changing
Revolution in the scientific workflow: many interfaces to shared CI services
Large
Facilities
Researcher
Software
Data
Shared Data/Software
Gateway Resources
Collaboration
Networks
Psdgraphics.com
National Computing
Resources
Cloud
Services

13. Scientific Discovery & Innovation Cyberinfrastructure Ecosystem
NSF embraces an expansive view of cyberinfrastructure motivated by scientific priorities, the changing conduct of science and informed by technology advances
OBSERVE
HYPOTHESIZE
EXPERIMENT
ANALYZE
THEORIZE
Scientific Discovery & Innovation
Data
Software
People, organizations,
& communities
Scientific
Instruments
Cyberinfrastructure Ecosystem
Computational
Resources
Networking &
Cybersecurity

14. NSF role in NSCI: Enduring Computational Ecosystem for Advancing Science and Engineering
Infrastructure Platform Pilots, Development and Deployment
Computational and Data Literacy across all STEM disciplines
Computational and Data Enabled Science and Engineering Discovery
Fundamental Research in HPC Platform Technologies, Architectures, and Approaches

15. NSF Implementation Community Input NSF-wide activity
NSF Advisory Committee(s) engagement
NSF-Wide Cyberinfrastructure Advisory Committee (ACCI)
Directorate ACs: MPS, CISE, …
National Academies Study completion
NSF-wide activity
CI Council
ACI, Heads of Directorates
Cross-Directorate Working Group
MPS & CISE/ACI Co-chair/leads

16. Discussion, Questions?