Center for Computational Sciences O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY...

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OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

Vision for OSC Computing and Computational Sciences

http://www.ccs.ornl.gov/

Thomas ZachariaAssociate Laboratory Director

Computing and Computational SciencesOak Ridge National Laboratory

Earth Simulator Rapid Response Meeting May 15-16, 2002

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Charge from Dr. Orbach

• Review “. . . current state of the national computer vendor community relative to high performance computing”

• “. . . Vision for what realistically should be accomplished in the next five years within the Office of Science in high performance computing”

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Dr. Orbach’s Vision for OSC ComputingStatement to ASCAC Committee, May 8, 2002

• “… there is a centrality of computation in everything that we do”

• “… large scale computation is the future of every program in the Office of Science”

• “… we want to have our own computing program in non-defense computational science”

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FY 03 Budget Request for OSC Computing Considerably Lower

than Required to Meet Goals

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As Fraction of Total Budget, OSC is Half NNSA and NSF and Needs Significant

Increase to Meet Goals

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Earth Simulator has Heightened Urgency for Infrastructure Strategy for

Scientific ComputingCritical Steps:• Invest in critical software

with integrated science, and computer science development teams

• Deploy scientific computing hardware infrastructure in support of “large scale computation”– Cray, HP, IBM, SGI– IBM is the largest US

installation• Develop new initiative to

support advanced architecture research

Top 500 SupercomputersUS has been #1 in 12 of 19 lists

A concerted effort will be required to regain US leadership in high performancecomputing. The LINPACK benchmark generally overestimates the effectivenessof an architecture for applications such as climate by a substantial factor. Stability and reliability are also important system properties.

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Invest in Critical Software with Integrated Science and Computer Science

Development Teams SciDAC: a Good Start Towards Scientific Computing Software

• Scientific Applications– Climate Simulation– Computational Chemistry– Fusion – 5 Topics– High Energy Nuclear

Physics – 5 Topics• Collaboratories

– Four Projects• Middleware & Network

Research– Six Projects

• Computer Science

– Scalable Systems Software

– Common Component Architecture

– Performance Science and Engineering

– Scientific Data Management

• Applied Mathematics

– PDE Linear/Nonlinear Solvers and Libraries

– Structured Grids/AMR

– Unstructured Grids

Dave Bader, SciDAC PI Meeting, Jan 15, 2002, Washington DC

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Deploy Scientific Computing Hardware Infrastructure to Support

“Large-Scale Computation”• Provide most effective and efficient computing resources for a set

of scientific applications• Serve as focal point for scientific research community as it adapts

to new computing technologies• Provide organizational framework needed for multidisciplinary

activities– Addressing software challenges requires strong, long term

collaborations among disciplinary computational scientists, computer scientists, and applied mathematicians

• Provide organizational framework needed for development of community codes– Implementing many scientific codes requires wide range of disciplinary

expertise

• Organizational needs will continue to grow as computers advance to petaflops scale

Dave Bader, SciDAC PI Meeting, Jan. 15, 2002, Washington, DC

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Earth Simulator has Widened Gap with DOE Scientific Computing Hardware Infrastructure

Top left: comparison between ES and SC resources – highlights widening gap between SC capabilities and others

Top right: comparison between ES and US resources of comparable peak performance – highlights architectural difference and need for new initiative to close the gap

Right: comparison between ES and US resources of comparable cost

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Possible U.S. Response in the Near Term for Increased Computing Capacity

• 40 TFlops Peak• 5120 Vector Processors• 8 GFlops Processor• 8 Processors per Node• $500 M Procurement• $50M/yr Maintenance• Limited Software Investment to date• Significant Ancillary Impact on

Biology, Nanoscience, Astrophysics, HENP, Fusion

• 40 TFlops Peak• 5120 Power5 Processors• 8 GFlops Processor• 64 Processors per Node• $100 M Procurement• $10M/yr Maintenance• SciDAC Investment in Computational

Science and related ISICs• Significant Ancillary Impact on

Biology, Nanoscience, Astrophysics, HENP, Fusion

Earth Simulator US Alternative

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Best Performance of High Resolution Atmospheric Model

Inter-node bandwidth (Mb/s)

Performance of Hi-Resolution Atmospheric Model

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SP3 WHII (512)T3E (512)

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QCDSPColumbia QCDOC

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ASCI White ASCI Compaq

Develop New Initiative to Support Advanced Architecture:

BlueGene Offers Possible Option

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QCDSP (600GF based on Texas Instruments DSP C31)– Gordon Bell Prize for Most Cost Effective Supercomputer in '98– Columbia University Designed and Built– Optimized for Quantum Chromodynamics (QCD)– 12,000 50MF Processors– Commodity 2MB DRAM

QCDOC (20TF based on IBM System-on-a-Chip)– Collaboration between Columbia University and IBM Research– Optimized for QCD– IBM 7SF Technology (ASIC Foundry Technology)– 20,000 1GF processors (nominal)– 4MB Embedded DRAM + External Commodity DDR/SDR SDRAM

BlueGene L/D (180TF based on IBM System-on-a-Chip)– Designed by IBM Research in IBM CMOS 8SF Technology– 64,000 2.8GF processors (nominal)– 4MB Embedded DRAM + External Commodity DDR SDRAM

BlueGene Architecture is a (more) General Purpose Machine that builds on QCDOC

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Host System:– Diagnostics, booting, archive– Application dependent requirements

System Organization (conceptual)

File Server Array– ~ 500 RAID PC servers– Gb Ethernet and/or Infiniband– Application dependent requirements

BlueGene/L Processing Nodes81920 Nodes– Two major partitions– 65536 nodes production– Platform (256 TFlops peak)– 16384 nodes partitioned into code

development platforms

Host Computer

Top View of system

50 Feet

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Summary• Continue investment in critical software with integrated

science, and computer science development teams

• Deploy scientific computing hardware infrastructure in support of “large scale computation”

• Develop new initiative to support advanced architecture research

• Develop a bold new facilities strategy for OSC computing

• Increase OSC computing budget to support outlined strategy

Without sustained commitment to scientific computing,

key computing and computational sciences capabilities,

including personnel, will erode beyond recovery.

Center for Computational Sciences O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY...

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