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Computer Science and Computational Science. Sampath Kannan, Division Director Computing & Communication Foundations Division National Science Foundation Outline. Need for new technology Challenges from the new technology Bridging the two disciplines NSF/CISE Programs.

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computer science and computational science
Computer Science and Computational Science

Sampath Kannan, Division Director

Computing & Communication Foundations Division

National Science Foundation

  • Need for new technology
  • Challenges from the new technology
  • Bridging the two disciplines
  • NSF/CISE Programs
the challenge a right hand turn in moore s law growth

AMD Phenom

Intel Woodcrest

The Challenge: “a right hand turn in Moore’s Law growth”


Thread Performance

“right hand turn” ascribed to P. Otellini, Intel


big scientific problems
Big Scientific Problems
  • Understanding oceans, atmosphere, climate:
    • more sensors for better accuracy -> more data
    • Coupled systems -> more complex computation
  • Biology and medicine:
    • Biology generating lots of data – per individual not per species; 2) metagenomics
    • Smart health: Personalized, ubiquitous health care; telemedicine, telepresence
  • Astrophysics, cosmology
  • … and many others
data deluge wsj aug 28 2009
Data Deluge: WSJ Aug 28, 2009
  • Never have so many people generated so much digital data or been able to lose so much of it so quickly, experts at the San Diego Supercomputer Center say
  • Computer users world-wide generate enough digital data every 15 minutes to fill the U.S. Library of Congress
  • More technical data have been collected in the past year alone than in all previous years since science began, says Johns Hopkins astrophysicist Alexander Szalay
  • The problem is forcing historians to become scientists, and scientists to become archivists and curators
  • Hardware
  • Middleware I/O, Storage, …
  • Software
  • Abstractions and formal reasoning
  • Algorithms
  • Power/Energy
  • Resilience to faults
variety of hardware platforms
Variety of Hardware Platforms
  • Multicore, many core:
    • How many? How heterogeneous?
    • What interconnects? What memory hierarchy?
  • Non-silicon: bio, nano, quantum

Even if applications can be designed for just one of these… computer science demands one (or a few) programming models.

middleware i o storage
Middleware, I/O Storage
  • Better Distributed Operating Systems
  • Better compilers (automatic parallelism detection, optimization, etc.)
  • Better I/O and intelligent storage systems

… should lead to …


  • Need good programming models
  • Need multiple levels of abstraction for
    • Expert programmers
    • Non-experts
  • Tools for reasoning about correctness and other properties
  • Tools and middleware that allow portability
energy power efficiency is critical
Energy/Power Efficiency is Critical
  • Power is bottleneck for HPC systems
    • Current systems consume 10’s of MWs of power
    • Costs to operate may be prohibitive
    • Power needed to cool a system approaches the power consumed by the system
    • System failure rate doubles for every 10° C rise in temperature
    • Reducing energy footprint of IT is important goal
fault resilience
Fault resilience
  • Not acceptable to deal with faults by hardware replication
  • Expose faults to as high a layer as possible and find robust computing solutions by combination of software and hardware approaches
computational vs computer science
Computational vs Computer Science
  • Computational Science Goal and Approach:
    • Solve important scientific problems of ever increasing scale
    • Ok if codes are designed for specific platform and application
    • A few standard Simulators and Equation Solvers slightly customized for application and platform
what computer science would like
What Computer Science would like
  • Problems specify what should be computed… not how it should be computed… to allow algorithmic and implementation ingenuity
  • Use good, existing software engineering ideas… and seek new ones appropriate for application
  • Solve the challenges in the earlier slides, so that a more generic infrastructure is created for hardware and software layers in HPC
what computer scientists should do
What Computer Scientists Should Do
  • Be a more dependable partner – provide software and tools that are maintained and evolved as needed
  • Understand the domain science issues
  • Appreciate the importance of specific applications
  • Appreciate the importance of computing and data as the 3rd and 4th paradigms of science… and the responsibility this gives them
cise programs core
CISE Programs - Core
  • Software + Hardware Foundations (≈ $40 – 50M /per year) supports
    • High Performance Computing
    • Compilers
    • Programming Languages
    • Formal Methods
    • Computer Architecture
    • Nanocomputing
    • Design Automation
other cise programs
Other CISE Programs
  • Computing Research Infrastructure (CRI) … recognizes that software is infrastructure
  • Expeditions in Computing: Our program for bold, ambitious, collaborative research: Upto 3 5-year projects per year, each funded at $10M.
programs with oci 1 hecura
Programs with OCI – 1) HECURA
  • Competitions in FY ’06, ‘08, ’09:
  • I/O, File Systems, Compilers, Programming Models, Compilers
  • $10 – 15M each year
  • Not sure when the next competition will be
3 software institutes for sustained innovation
3) Software Institutes for Sustained Innovation

Creating, maintaining, and evolving software forscientific computing

OCI is lead; CISE + Other Directorates participate

Current competition has small awards only

Workshops sought this year to lay groundwork forlarge, “Institute” awards in future years

cyber enabled discovery and innovation cdi
Cyber-Enabled Discovery and Innovation (CDI)
  • 3rd year of competition ≈$100 M each year
  • Agency-wide
  • Supports projects that advance
    • Two or more disciplines
    • Use of computational thinking
  • Many supported projects are in the area of scientific computing
  • CISE perspective guided by belief that:
    • Today’s High-Performance Computer is tomorrow’s general-purpose computer
    • We must keep developing general ideas that will allow for efficacious use of such machines broadly
    • We cannot predict where the need for these machines will be greatest
    • But today’s science applications are clearly pressing and important