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Testing PanDA at ORNL

Testing PanDA at ORNL. Danila Oleynik University of Texas at Arlington / JINR PanDA Workshop @ UTA 3-4 of September 2013. Outline. Current PanDA implementation HPC at ORNL architecture, specialty PanDA architecture for Kraken, Titan PanDA Pilot modification to cope HPC SAGA API

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Testing PanDA at ORNL

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  1. Testing PanDA at ORNL Danila Oleynik University of Texas at Arlington / JINR PanDA Workshop @ UTA 3-4 of September 2013

  2. PanDA workshop @ UTA Outline • Current PanDAimplementation • HPC at ORNL architecture, specialty • PanDA architecture for Kraken, Titan • PanDA Pilot modification to cope HPC • SAGA API • Initial testing • In progress: Pilot - SAGA integration.

  3. PanDA workshop @ UTA Current PanDAimplementation • One Pilot per WN • Pilot executes on same node as job • SW distribution through CVMFS

  4. PanDA workshop @ UTA HPC at ORNL architecture, specialty • Kraken Cray XT5 • 9408 nodes • node: 12 core, 16 GB RAM • Titan Cray XT7 • 18,688 nodes • node: 16 core, 32 + 6 GB RAM (2GB per core) • One-Time Password Authentication • Parallel file system shared between nodes. • Extremely limited access to worker nodes • Internal job management tool: PBS/TORQUE • One job occupy minimum one node (12-16 cores) • Limitation of number of jobs in scheduler for one user: slots limitation • Own compilers, interpreters, libraries

  5. PanDA workshop @ UTA PanDA architecture for Kraken, Titan • Pilot(s) executes on HPC interactive node • Pilot interact with local job scheduler to manage job • Number of executing pilots should be equal or less of number of available slots in local scheduler

  6. PanDA workshop @ UTA PanDA Pilot modification to cope HPC • Main modification of Pilot for working with HPC, is changing in part of Job executing process (runJob class) • Environment validation procedures should be checked. • Stage In/Stage Out procedures should be checked.

  7. PanDA workshop @ UTA SAGA API • SAGA (Simple API for Grid Applications) defines a high-level interface to the most commonly used distributed computing functionality. SAGA provides an access-layer and mechanisms for distributed infrastructure components like job schedulers, file transfer and resource provisioning services • Behind the API facade, SAGA-Python implements a flexible adaptor architecture. Adaptors are dynamically loadable modules that interface the API with different middleware systems and services. • Developed and intensive maintained by RADICAL Research at The Cloud and Autonomic Computing Center, Rutgers University. • http://saga-project.github.io

  8. PanDA workshop @ UTA SAGA API. Supported Middleware • Job Submission Systems • SSH and GSISSH • Condor and Condor-G • PBS/Torque • Sun Grid Engine • SLURM • File / Data Management • SFTP/GSIFTP • HTTP/HTTPS • Resource Management / Clouds • EC2 (libcloud)

  9. PanDA workshop @ UTA SAGA API. Typical schema

  10. PanDA workshop @ UTA SAGA API. Otherfeatures • Configuration and logging • Arguments and environment setup for jobs execution in batch processing systems • Different security contexts: • UserPass • SSH • X.509 • MyProxy • EC2 • EC2_KEYPAIR

  11. PanDA workshop @ UTA Initial testing • Initial testing was done for proving that PanDA components will be able to run in HPC environment on interactive nodes • APF and Pilot was successfully started on HPC • Outbound https connection was confirmed, so pilots can communicate with PanDA server • SAGA API was successfully tested on HPC for managing jobs in local job scheduler • Due to interactive node and worker nodes use shared file-system, we did not need any special internal data-management process • Connection from Titan FE to Federated ATLAS Xrootd is verified

  12. PanDA workshop @ UTA Initial testing

  13. PanDA workshop @ UTA In progress: Pilot modification • Though Pilot was run on HPC and communication with PanDA server was established, procedures for checking environment for specific experiment for the moment fails. • In process modification of this procedures • Next steps: • Validation of stage in/stage out procedures • Validation of simple job execution on HPC head node • Development of runJodHPC class, to manage job in PBS/Torque job scheduler.

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