Page History

UltraScan data analysis application is a software package that is able to take advantage of remote computational resources in order to support the interpretation of analytical ultracentrifugation experiments. Since 2006, the UltraScan scientific gateway has been used by scientists studying the solution properties of biological and synthetic molecules. UltraScan supports its users with a scientific gateway (LIMS) in order to leverage the power of supercomputing. LIMS provides access for data associated with sedimentation experiments and allows sharing of data with collaborators. Authorized users can use this site to model analytical ultracentrifugation experiments with UltraScan's high-performance analysis modules by submitting analysis jobs to XSEDE and campus computing clusters.Image Removed

Image Added

Use Cases Summary: 

1. User prepares data through Desktop GUI as before.

2. User logs into LIMS portal.

3. Through the portal, the user creates a Queue (task object) for an analysis task.

4. User submits job.

5. LIMS invokes Airavata through the Airavata API.

1. Tarred data is sent as before.

2. Airavata stores in temporary storage.

3. Resource parameters are sent through the API.

1. Parameters include hostname, host count, processor count, wall time, etc.

2. No XML file used is used for Airavata Scheduling. Any information needed by application will be propogated. The key point is Airavata will provide a generic way of providing scheduling information.

6. Airavata notifies LIMS if success or failure.

1. What should be returned?  Ticket (workflow instance ID) that can be used to get JobIDs later. JobID is the job ID generated by the queue.

7. Airavata stages the input tar to the destination resource.

8. Airavata  submits the analysis application on the remote resource provider.

1. We want to use GSI-SSH instead of GRAM

9. The remote resource provider returns a job ID.

10. Airavata monitors the job’s status with its job ID.  

1. This is persistently stored

11. LIMS queries Airavata through the API to get job ID using the Ticket

12. LIMS queries Airavata through the API to job status using Job ID and/or Ticket

13. Analysis Application starts to run on the remote resource.

14. Analysis Application sends UDP messages to Airavata

1. UDP messages contain application state information and metadata

15. User uses LIMS query tool to find application state.

16. LIMS Query Tool calls Airavata API to get application state (both job status and internal state of the application)

1. This should be stored in the Airavata Registry

2. It would also be possible to push these using Airavata’s messenger.

17. The job completes.

18. Airavata monitoring detects that the job has completed.

1. Update status in registry.

2. Airavata pulls data from remote resource.

19. Airavata notifies LIMS that job has completed and data is ready.

20. LIMS pulls outputs back to AUC DB

21. Email notification is sent to user about the job status by LIMS.

22. User pull the data to the desktop tool for further analysis.