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SciDAC-2 Petascale Data Storage Institute

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SciDAC-2 Petascale Data Storage Institute. Philip C. Roth Computer Science and Mathematics Future Technologies Group. The petascale storage problem. Petascale computing makes petascale demands on storage Performance Capacity Concurrency Reliability Availability Manageability - PowerPoint PPT Presentation
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Presented by SciDAC-2 Petascale Data Storage Institute Philip C. Roth Computer Science and Mathematics Future Technologies Group
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Page 1: SciDAC-2 Petascale Data Storage Institute

Presented by

SciDAC-2Petascale Data Storage Institute

Philip C. RothComputer Science and Mathematics

Future Technologies Group

Page 2: SciDAC-2 Petascale Data Storage Institute

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Petascale computing makes petascale demands on storage

Performance

Capacity

Concurrency

Reliability

Availability

Manageability

Parallel file systems are barely keeping pace at terascale; the challenges will be much greater at petascale

Cray XT3

Cray X1E

The petascale storage problem

at ORNL

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Petascale Data Storage Institute

The PDSI is an institute in the Department of Energy (DOE) Office of Science’s Scientific Discovery through Advanced Computing (SciDAC-2) program

Using diverse expertise with applications and fileand storage systems, members will collaborate on requirements, standards, algorithms, analysis tools

Led by Dr. Garth Gibson, Carnegie Mellon Universityhttp://www.pdl.cmu.edu/PDSI

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Carnegie Mellon University

Participating Institutions

Lawrence Berkeley National Laboratory/NERSC

Los Alamos National Laboratory

Pacific Northwest National Laboratory

Sandia National Laboratories

Oak Ridge National Laboratory

University of California at Santa Cruz

University of Michigan at Ann Arbor

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Novel Storage Mechanisms Novel Storage Mechanisms

IT Automation IT Automation

Standards and APIs Standards and APIs

Community Building Community Building

Failure Data Collection Failure Data Collection

Performance Data Collection Performance Data Collection

Petascale Data Storage Institute agenda

Collection Collection

Dissemination Dissemination

Innovation Innovation

Main Thrusts Projects

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Collection: Performance analysis

Performance data collection and analysis

Workload characterization

Benchmark collection and publication

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Led by William Kramer,National Energy ResearchScientific Computing Center (NERSC)

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0 10 20 30 40 50 600

10

30

40

50

60

70

80

20

Months in production use

Fai

lure

s p

er m

on

th

UnknownHumanEnvironmentNetworkSoftwareHardware

http://institutes.lanl.gov/datahttp://www.pdl.cmu.edu/FailureData

Collection: Failure analysis

Capture and analyze failure, error, and usage data from high-end computing systems

Initial example: Los Alamos failure data available for 22 systems over 9 years with extensive analysis by Bianca Schroeder, Carnegie Mellon University

Led by Gary Grider, Los Alamos National Laboratory

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Dissemination: Outreach

Our approach Workshops (SC06 PDSI workshop, November 17!) Tutorials and course materials Online, open repository with documents, tools, performance

and failure data

Target audience Computational scientists Academia (professors and students) Industry (storage researchers and developers)

Led by Dr. Garth Gibson, Carnegie Mellon University

Goal: to disseminate information about techniques, mechanisms, best practices,

and available tools

Goal: to disseminate information about techniques, mechanisms, best practices,

and available tools

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Dissemination: Standards and APIs

Some work underway POSIX extensions

E.g., support for weak data and metadata consistency http://www.pdl.cmu.edu/POSIX

Parallel Network File System (pNFS) In IETF NFSv4.1 standard draft University of Michigan Center for Information Technology

Integration producing reference implementation http://www.pdl.cmu.edu/pNFS

Led by Gary Grider, Los Alamos National Laboratory

Goals: to facilitate standards development and deployment and to validate and demonstrate

new extensions and protocols

Goals: to facilitate standards development and deployment and to validate and demonstrate

new extensions and protocols

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Innovation

IT automation appliedto high-end computing systems and problems

Novel mechanisms forcore high-end computingstorage problems

Storage system instrumentationfor machine learning

Data layout andaccess planning

Automated diagnosis,tuning, failure recovery

WAN/global storage access

High performancecollective operations

Rich metadata at scale

Integration with system virtualization technology

Led by Dr. Garth GibsonCarnegie Mellon University

Led by Darrell LongUniversity of California at Santa Cruz

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Summary

The Petascale Data Storage Institute brings together individuals with expertise in file and storage systems, applications, and performance analysis

PDSI will be a focal point for computational scientists, academia, and industry for storage-related information and tools, both within and outside SciDAC

http://www.pdl.cmu.edu/PDSI

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ORNL contact

Philip C. RothFuture Technologies GroupComputer Science and Mathematics(865) [email protected]

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