scientific computing for slac science

Scientific Computing for SLAC Science

Bebo White

Stanford Linear Accelerator Center

October 2006

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Scientific ComputingThe relationship between Science and the

components of Scientific Computing Application Sciences

Issues addressable with “computing”

Computing techniques

Computing hardware

High-energy and Particle-Astro Physics, Accelerator Science, Photon Science …

Particle interactions with matter, Electromagnetic structures, Huge volumes of data, Image processing …

PDE Solving, Algorithmic geometry, Visualization, Meshes, Object databases, Scalable file systems …

Processors, I/O devices, Mass-storage hardware, Random-access hardware, Networks and Interconnects …

Computing architectures

Single system image, Low-latency clusters, Throughput-oriented clusters, Scalable storage …

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Drivers for SLAC Computing

• Computing to enable today’s data-intensive science

– Clusters, interconnects, networks, mass storage, etc.

• Computing research to prepare for tomorrow’s challenges

– Massive memory, low latency, petascale databases, detector simulation, etc.

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SLAC Scientific ComputingScience Goals Computing Techniques

BaBar Experiment (winds down 2009-2012)

Measure billions of collisions to understand matter-antimatter asymmetry (why matter exists today)

High-throughput data processing, trivially parallel computation, heavy use of disk and tape storage. Intercontinental distributed computing.

ATLAS Experiment and Experimental HEP

Analyze petabytes of data to understand the origin of mass

High-throughput data processing. trivially parallel computation, heavy use of disk and tape storage. Intercontinental distributed computing.

Accelerator Science Simulate accelerator behavior before construction and during operation

Parallel computation, visual analysis of large data volumes

Particle Astrophysics (mainly simulation)

Star formation in the early universe, colliding black holes, …

Parallel computation (SMP and cluster), visual analysis of growing volumes of data

Particle Astrophysics Major Projects (GLAST, LSST …)

Analyze terabytes to petabytes of data to understand the dark matter and dark energy riddles

High-throughput data processing, very large databases, visualization

Photon Science Femtosecond x-ray pulses, “ultrafast” science, structure of individual molecules …

High throughput data analysis and large-scale simulation

New Architectures for SLAC Science

Radical new approaches to computing for Stanford-SLAC data-intensive science

Current focus: massive solid-state storage for high-throughput, low-latency data analysis

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Data Challenge in High Energy Physics2006 example

SLAC

Online System

Selection and Compression

~10TB/s

• Raw data written to tape:10MB/s• Simulated and derived data: 20 MB/s• International network data flow to “Tier A Centers” 50 MB/s (400Mb/s)

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Tier 1

Online System

EventReconstruction

France Germany

Institute ~0.25TIPS

~100 MBps

~0.6-2.5 Gbps

100 - 1000 Mbps

Physics data cache

~PBps

~0.6-2.5 Gbps

Tier 0 +1

Tier 3

Tier 4

Tier 2

• 2000 physicists in 31 countries are involved in this 20-year experiment in which DOE is a major player.

• Grid infrastructure spread over the US and Europe coordinates the data analysis

Analysis

Italy FermiLab, USA

Data Challenge in High Energy Physics: CERN / LHC High Energy Physics Data 2008 onwards

Event Simulation

CERN LHC CMS detector12,500 tons, $700M 2.5-40 Gbps

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Client Client Client Client Client Client

Disk Server

Disk Server

Disk Server

Disk Server

Disk Server

Disk Server

Tape Server

Tape Server

Tape Server

Tape Server

Tape Server

SLAC-BaBar Computing Fabric

IP Network (Cisco)

IP Network (Cisco)

120 dual/quad CPU Sun/Solaris~700 TB Sun RAID arrays (FibreChannel +some SATA)

1700 dual CPU Linux (over 3700 cores)

25 dual CPU Sun/Solaris40 STK 9940B6 STK 9840A6 STK Powderhornover 1 PB of data

HEP-specific ROOT software (Xrootd) + Objectivity/DB object database some NFS

HPSS + SLAC enhancements to ROOT and Objectivity server code

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Used/Required Space

Space (Square Feet)

0

5000

10000

15000

20000

25000

30000

35000

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Non-scientific Space

Low Density space

Medium Density space

Ultra High Density space

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DOE Lab-International R&E

Lab-U.S. R&E (domestic)

Lab-Lab (domestic)

Lab-Comm. (domestic)

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Growth and Diversification

• Continue shared cluster growth as much as possible

• Increasing MPI (parallel) capacity and support (astro, accelerator, and more)

• Grid interfaces and support (Atlas et.al)

• Large SMPs (Astro)

• Visualization

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Research - PetaCache

• The PetaCache architecture aims at revolutionizing the query and analysis of scientific databases with complex structure

– Generally this applies to feature databases (terabytes-petabytes) rather than bulk data (petabytes-exabytes)

• The original motivation comes from HEP

– Sparse (~random) access to tens of terabytes today, petabytes tomorrow

– Access by thousands of processors today, tens of thousands tomorrow

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Latency Ideal

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Latency Current Reality

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Latency Practical Goal

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PetaCache Summary

• Data-intensive science increasingly requires low-latency access to terabytes or petabytes

• Memory is one key:– Commodity DRAM today (increasing total cost by ~2x)

– Storage-class memory (whatever that will be) in the future

• Revolutions in scientific data analysis will be another key– Current HEP approaches to data analysis assume that random

access is prohibitively expensive

– As a result, permitting random access brings much-less-than-revolutionary immediate benefit

• Use the impressive motive force of a major HEP collaboration with huge data-analysis needs to drive the development of techniques for revolutionary exploitation of an above-threshold machine

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Research – Very Large Databases

• 10-year, unique experience with VLDB

– Designing, building, deploying, and managing peta-scale production datasets/database – BaBar – 1.4 PB

– Assisting LSST (Large Synoptic Survey Telescope) in solving data-related challenges (effort started 4Q 2004)

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LSST – Data Related Challenges (1/2)

• Large volumes

– 7 PB/year (image and catalog data)

– 500 TB/year (database)• Todays VLDBs ~10s TB range

• High availability

– Petabytes -> 10s of 1000s of disks -> daily disk failures

• Real time requirement

– Transient alerts generated in < 60 sec

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LSST – Data Related Challenges (2/2)

• Spatial and temporal aspects

– Most surveys focus on a single dimension

• All data made public with minimal delay

– Wide range of users – professional and amateur astronomers, students, general public

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VLDB Work by SCCS

• Prototyping at SCCS

• Close collaboration with key MySQL developers

• Working closely with world-class database gurus

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Research – Geant4

• A toolkit simulating elementary particles passing through and interacting with matter, and modeling the detector apparatus measuring the passage of elementary particles and recording the energy and dose deposition

• Geant4 is developed and maintained by an international collaboration

– SLAC is the second largest center next to CERN

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Acknowledgements

• Richard Mount, Director, SCCS

• Chuck Boeheim, SCCS

• Randall Melen, SCCS

WWW 2008 April 21-25, 2008

Beijing, China

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Host Institution and Partners• Beihang University

– School of Computer Science

• Tsing-Hua University, Peking University, Chinese Academy of Sciences, …

• Microsoft Research Asia

• City Government of Beijing (pending)

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BICC: Beijing International Convention Center

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Key Personnel• General Chairs:

– Jinpeng Huai, Beihang University

– Robin Chen, AT&T Labs

• Conference Vice Chair:

– Yunhao Liu, HKUST

• Local volunteers

– 6-10 grad students led by Dr. Zongxia Du

– In cooperation with John Miller (TBD)

• IW3C2 Liaison: Ivan Herman

• PCO: two candidates under consideration

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Local Organizing Committee• Composition of Local Organizing Committee:

– Vincent Shen, The HK University of Science and Technology

– Zhongzhi Shi, Chinese Academy of Sciences

– Hong Mei, Peking University

– Dianfu Ma, Beihang University

– Guangwen Yang, Tsinghua University

– Hsiao-Wuen Hon, Microsoft Research Asia

– Minglu Li, Shanghai Jiao Tong University

– Hai Jin, Huazhong University of Science and Technology

– … and Chinese Internet/Software/Telecom companies

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