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Lustre -WAN : Enabling Distributed Workflows in Astrophysics

Lustre -WAN : Enabling Distributed Workflows in Astrophysics . Scott Michael scamicha@ indiana.edu. April 2011. Studying Planet Formation. My research group investigates the formation of giant planets There are more than 500 extra-solar gas giants that have been discovered to date

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Lustre -WAN : Enabling Distributed Workflows in Astrophysics

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  1. Lustre-WAN: Enabling Distributed Workflows in Astrophysics Scott Michael scamicha@indiana.edu April 2011

  2. Studying Planet Formation • My research group investigates the formation of giant planets • There are more than 500 extra-solar gas giants that have been discovered to date • Investigations are carried out using a 3D radiative hydrodynamics code • Each simulation takes several weeks and produces 5-10 TB of data • A series of 3-5 simulations is needed for each study • Data is analyzed to produce a variety of measurements

  3. Our Workflow Before Using Lustre-WAN • Our workflow has three parts • Simulation – shared memory • Analysis – distributed memory • Visualization – proprietary software with interactivity • In the past we have transferred data between HPC resources, stored the data locally, and performed analysis and visualization

  4. Using a Central Lustre File System • Using Indiana University’s Lustre based Data Capacitor WAN file system different systems in separate parts of the workflow can all access the same data • Data generated by SGI Altix • 4 simulations on NCSA’s Cobalt • 6 simulations on PSC’s Pople • Data analyzed by departmental resources and distributed memory machines • Indiana University’s Big Red and Quarry • Mississippi State University’s Raptor and Talon • Data visualization by departmental machines • Indiana University IDL license

  5. Using a Central Lustre File System

  6. Use Cases for Lustre WAN • Many cases where researcher needs resources outside a single data center • This is increasingly common in the TeraGrid • A user needs to use heterogeneous resources • Shared and Distributed Memory • GPUs • Visualization systems • A researcher needs to capture instrument data • A researcher needs to migrate to a different system • Ideally every user can access all his data from any resource all the time

  7. Thanks To • MSU Research • Trey Breckenridge • Roger Smith • Joey Jones • Vince Sanders • Greg Grimes • PSC • NCSAThis material is based upon work supported by the National Science Foundation under Grant No. CNS-0521433 • DC Team • Steve Simms • Josh Walgenbach • Justin Miller • Nathan Heald • Eric Isaacson • IU Research Technologies • Matt Link • Robert Henschel • Tom Johnson

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