1. Virtual Geophysics Laboratory (VGL): Exploiting the Cloud and HPC
Ryan Fraser (CSIRO), Lesley Wyborn (GA), Richard Chopping (GA), Terry Rankine (CSIRO), Robert Woodcock (CSIRO)
Minerals down under

2. Scientific workflow – Virtual Geophysics Laboratory (VGL)
Scientific Workflow Engine (or Virtual Laboratory)
Automates and massively expands (GA) Geophysicists computational capacity via the Cloud
Amazon – EC2 / S3 (and others using this interface)
OpenStack
Collaboration between CSIRO, GA, NCI, Monash, UQ, and ANU
VGL is just a pretty face
User Driven GUI
Leverages data providers and cloud technologies to do all the heavy lifting
Non-Restrictive Open-source
Ask audience about their level of experience with the cloud (or general HPC)
Explain the cloud (if required)
Computation
Storage

3. V(what)GL VEGL – Virtual Exploration Geophysics Laboratory
Done
VEGL – Virtual Exploration Geophysics Laboratory
One primary science collaboration
One primary workflow
One collection of geophysical data sets
VGL - Virtual Geophysics Laboratory
NeCTAR funded activity
Collaboration with multiple partners (CSIRO, NCI, GA, UQ, Monash, ANU)
Supporting multiple workflows
New data sets
New data types
New Use – Not just exploration.
What is enabling workflow??
Its not about you, its about the next person

4. Geophysics (as seen by a software dev)
Apply Mathematics
Raw data
Geophysics is taking physical measurements…
Magnetism over an area
Acceleration due to gravity over an area
…Applying lots of mathematics…
…to infer the structure of what is under the surface…
It is not geology
Samples are never taken, only measurements
Crash course in geophysics
Integration – where I tuned out
But -- Where is all the ????

5. Our Geophysics Problem
Measurements coming from the field are ‘raw’
Varying spatial reference systems
Noisy
Artefacts from collection process
This data needs processing
From raw data to a data product
Data products are valuable
They will be re-used and referenced repeatedly
Processing is a time consuming process
Made worse by a purely manual workflow
Scientific models usually require strict input conditions

6. The Past Compile raw data using proprietary FORTRAN
Also use software – Intrepid
Transform to a regular grid using more software
MATLAB, Intrepid, ER Mapper, ESRI ArcGIS, QGIS
Crop data spatially to suit final data product
eg: everything in Victoria
Transform data into a file format that can be read by proprietary scientific code.
This is usually done with some handwritten python or c
There is no version control, code is often rewritten / redone
Upload data to HPC
Manually enter input parameters/start job

7. Let’s map it out… Hardcopy of data Intrepid MATLAB
Get handed field data
Visualise data
Transform to a regular grid
Download results
Crop data to area of interest
Reformat data for processing
SSH Client
Configure job and start processing
Upload data to NCI

8. There seems to be a problem…
Reproducibility – there is none
What was the input of your model?
What transformations occurred?
It’s a manual process
Time consuming
Error prone
Expensive
Licensing costs
If you are making big decisions – you want confidence in your evidence
Data products can be interpretations - subjective – even more important to know its source
Documentation

9. The Recent Past - Our solution
Virtual Exploration Geophysics Laboratory - Portal
Provenance (“I hate this word”)
All input data is saved and then published with the final data product
VEGL automates portions of the workflow
Allowing scientists to focus on science
Built entirely on open source tools
No licensing costs

10. Obligatory architecture diagram
CLOUD

11. Data Discovery All data discovered via registry
Data providers can be from anywhere in the world
Easy to integrate from multiple organisations
you can get more than geophysics information. You can get drillholes and sample information that could be used to constrain the geophysical inversion. There are advantages to having auscope infrastructure make finding all available data simpler.

12. Data Selection
Data can be subset easily, reprojected and reformatted (work done by data providers)

13. Script Builder Processing of the data involves file format conversion
science!!
Building blocks for common tasks
Reading/Writing to cloud storage
This is a “SHARED” script library -> shared improvements AND fixes
Version controlled for governance etc

14. Job Monitoring Multiple jobs can be run and monitored separately
Results can be published (Provenance information)

15. Published provenance records
This final step persists the results AND how we got there
* ticks data quality and reproducibility boxes

16. From this… Hardcopy of data Intrepid MATLAB Get handed field data
Visualise data
Transform to a regular grid
Download results
Crop data to area of interest
Reformat data for processing
SSH Client
Configure job and start processing
Upload data to NCI

17. Virtual Geophysics Laboratory
…to this
Virtual Geophysics Laboratory
Select spatial bounds
Build “science” from existing libraries
Discover raw data
Run job
Collect and publish results

18. VEGL – Point-of-View benefits
User:
Data all accessible in one place
Same science but MUCH more efficient
Bigger scale, quicker
Repeatability
Developer/Tech:
Its concepts can be re-used for other scientific workflows
It can produce actual scientific data products
Is capable of integrating with any SISS (OGC) data provider
The power lies with the underlying services, accessed using standardised protocols
Its concepts can be re-used for other scientific workflows
Is in the process of being deployed at GA
It can produce actual scientific data products
Is capable of integrating with any SISS data provider
Or any provider that understands the OGC standards
It’s built from many ‘generic’ components that can be repurposed
Is just a pretty face
The power lies with the underlying services
These services are accessed using standardised protocols
1) Steps 1 and 2, best summed up as Data prep (includes synthesising phys prop data as we can do that from within vegl already, just would need to enable access to borehole data)
2) Lets us do the same science much more efficiently. Hard to ensure repeatability if all the data prep, selection on various parameters etc is done by individuals without tracking of what they last did. Previously keeping track of all of it came from written notes etc rather than it being recorded somewhere automatically. Also a big problem with the inversions is people correctly or incorrectly preparing data: lots of ways to get it wrong, a lot less ways to do it right.

19. NOW - Opportunities: Virtual Geophysics Laboratory
Collaboration with multiple partners – CSIRO, GA, NCI, Monash, UQ, ANU
Supporting multiple workflows
Model Registry (3D)
New Scientific Codes – Underworld, eScript, UBC, Airborne EM inversion codes
New data sets from GA: National Airborne Geophysical DB including
Gravity, Radiometric, AEM, Magnetics
New Use – Broad application.

20. Opportunities Exploiting the generic
Modularising the workflow for general scientific usage
Repurposing for other use cases – nature hazards, climate prediction, etc
Commercial uptake
Integration with other VLs to achieve ultimate aim
Supercomputing - Pawsey Centre, NCI
Cloud – NeCTAR, NCI and commercial providers
NeCTAR VGL funded – opportunity to use and/or repurpose the platform for other uses
Essentially $20M capital expenditure for geosciences directed straight to HPC

21. Thank you and for more information:
CSIRO Earth Science & Resource Engineering
Ryan Fraser
Project Lead
Phone:
Web:
siss.auscope.org