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

3 The Challenge: “a right hand turn in Moore’s Law growth” DigitalMedia/43264A_hi_res.jpg AMD Phenom g 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 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

5 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 programmers  Non-experts  Tools for reasoning about correctness and other properties  Tools and middleware that allow portability

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 10

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 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  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 3 rd and 4 th paradigms of science… and the responsibility this gives them

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  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

2) PetaApps  Develop the future simulation, optimization and analysis tools that use emerging petascale computing  Will advance frontiers of research in science and engineering with a high likelihood of enabling transformative research  Areas examined include: - Climate Change -Earthquake Dynamics -Storm Surge Models -Supernovae simulations

3) Software Institutes for Sustained Innovation Creating, maintaining, and evolving software for scientific computing OCI is lead; CISE + Other Directorates participate Current competition has small awards only Workshops sought this year to lay groundwork for large, “Institute” awards in future years

Cyber-Enabled Discovery and Innovation (CDI)  3 rd 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

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