Computer Science and Computational Science

Sampath Kannan, Division Director Computing & Communication Foundations DivisionNational Science Foundation

skannan@nsf.gov

Outline

• Need for new technology• Challenges from the new technology• Bridging the two disciplines• NSF/CISE Programs

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

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AMD Phenom

http://www.intelstartyourengines.com/images/Woodcrest%20Die%20Shot%202.jpg

Intel Woodcrest

Single Thread Performance

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

Big Scientific Problems Understanding oceans, atmosphere,

climate: more sensors for better accuracy -> more

dataCoupled 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

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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

Challenges Hardware Middleware I/O, Storage, … Software Abstractions and formal reasoning Algorithms Power/Energy Resilience to faults

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 Better Distributed Operating Systems Better compilers (automatic parallelism

detection, optimization, etc.) Better I/O and intelligent storage

systems

… should lead to …

EASIER PROGRAMMING MODELS

Software Need good programming models Need multiple levels of abstraction for

Expert programmersNon-experts

Tools for reasoning about correctness and other properties

Tools and middleware that allow portability

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Energy/Power Efficiency is Critical

Power is bottleneck for HPC systemsCurrent systems consume 10’s of MWs of

powerCosts to operate may be prohibitivePower needed to cool a system approaches

the power consumed by the systemSystem failure rate doubles for every 10° C

rise in temperatureReducing energy footprint of IT is important

goal

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 Science Goal and Approach:Solve important scientific problems of ever

increasing scaleOk if codes are designed for specific

platform and applicationA few standard Simulators and Equation

Solvers slightly customized for application and platform

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

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 Software + Hardware Foundations (≈

$40 – 50M /per year) supportsHigh Performance ComputingCompilersProgramming LanguagesFormal MethodsComputer ArchitectureNanocomputing Design Automation

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 Competitions in FY ’06, ‘08, ’09: NSF (CISE+OCI), DARPA, DoE 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

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)

3rd year of competition ≈$100 M each year

Agency-wide Supports projects that advance

Two or more disciplinesUse of computational thinking

Many supported projects are in the area of scientific computing

Conclusion 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