Docstoc

FreeUSP and FreeDDS

Document Sample
FreeUSP and FreeDDS Powered By Docstoc
					         Exploration & Production Technology
      delivering breakthrough solutions




SEG HPC Summer Workshop 2011

Richard Clarke
Talk outline


• 10 years ago
• Bounce around the present
  − reflects an industry with lots of changing options
• A possible future?
~10 years ago…


• CM5 just retired
• We were using SGI Origins
• First trials with a Linux cluster
  − 32 dual cpu computers
  − Not using MPI (did it exist?)
  − “manual” job submission using rsh
  − manual reboot
• Mix of production processing & research
• ~99% utilization rate
  HPPC size = f(Moore’s law, oil price)


Flops
Memory
Disk
I/O bandwidth




                             Time
                                          4
 Today


• almost 5000 times bigger than 1999:
  − 450 teraflops
  − 3500 computers with over 40,000 CPUs
  − 230 terabytes of physical memory
  − 15 petabytes of raw disk storage
  − mix of large/medium memory systems


• used for applied research – NOT production processing
   − team of geophysicists, mathematicians, computer scientists
   − focus on quick development & application of new technology


• SGE job submission/monitoring


• Utilization is ~99%



                                                                  5
What keeps the computers busy?

• Geophysically:
  − Propagating waves forwards, backwards, up, down…
  − Summing waves and images together
  − Cross correlating waves and images
  − Solving Inverse Problems


• Mathematically:
  − FFT
  − Finite difference extrapolation
  − Convolutions / Cross correlation (often via FFT)
  − Linear algebra


• Computationally, we typically do:
  − something embarrassingly parallel, followed by
  − (maybe) transpose/summation and user QC,…
  − and then something else embarrassingly parallel,…
What keeps the users busy?

• The trivial stuff
  − Book-keeping
  − Summing
  − Sorting
  − Quality Control
     − How to best QC terabytes of data?
     − Diagnostic statistics help, but it is hard to replace the human eye
  − Debugging
     − Users often need to know algorithm implementation details to be
       able to run a program successfully
What keeps the developers busy?

• Debugging !
• 10 years ago:
  −   almost all our codes were written in Fortran77
  − researchers developed codes alone
  − debugging was done with print statements and Sun Workshop


• Today:
  − Almost all codes are written in Fortran77
      − a few are written in Fortran2003
      − the “Fortran-2003-ers” are sharing code!
      − parallelization is done with OpenMP and MPI
  − debugging is mostly done with print statements
      − debugging MPI codes is painful, even with debuggers
What do we use the large memory machines for?


• Open/interactive use
  − Shared by lots of users for doing the trivial stuff
  − Need lots of cpus, memory and for folks to “play nicely”
  − Avoids the “idle nodes waiting for large MPI job” user frustration


• “Debugging”
  − Initial R&D on large problems
  − It is much easier to debug, then optimize later with MPI, etc
Fault Tolerance


• Increasingly an issue
  − more machines means higher risk of faults
  − increasing use of local disk


• Not handled well with current MPI
GPU clusters & Cloud Computing


• Not currently using GPU clusters
  − sponsoring & monitoring external projects
  − large effort required for porting
  − short half-life of technology  strategy of longer access through vendors
  − energy requirements (we need a new building)


• Cloud computing
  − Upload 50Tb, compute, create 100Tb tmp files, download 50Tb ??
Challenges & Initial Conclusions


• Still lots of problems that are too expensive to run
  − Fewer approximations (anisotropic, elastic, visco-elastic)
  − Finer scale, larger datasets
• Still lots of problems that require lots of memory
  − Local disk use is growing as a stop gap fault tolerance is an issue
• Visualizing large (many Terabyte) datasets
  − Viz clusters are an option, but expensive for lots of users
• Steep learning curve for developing parallel applications



                 The standard conclusions: bigger, faster, easier, please.
Let’s have some fun…


• Connectivity to the end “user”
  − Keeping computers busy does not directly generate $
  − Need to get concise data to the end decision maker


• Let’s look at some examples:
  − A past example
  − An almost there example
  − A futuristic example…
  A field example: Valhall


• ~10 years ago
  − Processed the 1st 3D OBC survey at Valhall
  − Start to finish, processing times were about 12-18 months
  − 2 months to migrate ~25Gb data on 2 x 32cpu SGI machines
  − Chopped the input data into pieces, create pieces of the output, then summed it
  − Lots of approximations: isotropic, cause traveltime grids,… to speed it up


• In 2003 the idea of permanently installed sensors materialized…
  − The volume of data exploded: the first survey was 7Tb
  − A new survey is acquired every 4-6 months
Valhall Life of Field Seismic (LoFS), 2003-
present
 Pre-processing on the platform




                                               Data Recording


                                  Fastest turnaround: 4 days
                                  3.5Tb data/survey
                                  Still in use: 13 surveys acquired to date
                                  Enabled cross-discipline integration


                                  Imaging

                                                                              Data transfer
Now an “almost there” example…
Velocity Model Building




• Salt model building is still intensely manual
• A recent project imaged ~100 different models
• Modelling uncertainties
A futuristic example…
There’s an app for that…



• accelerometers
• wireless networks
• commodity
   If iphones could fly…


• Combine with the $20 toy from the local mall…
• Robotization research proposal from U. Delft.
• Perfect for rough terrain




• Similarly for marine:
  − this AUV from Rutgers U. crossed the Atlantic
…what if thousands could fly in formation…


• senseable.mit.edu/flyfire
…add HPC to get ‘imaging adaptive surveys’




                                            Redeploy sensors



                                                               Image
                     Image




      • complements rapid ISS acquisition
A few issues…

     Coupling…   Any Hitchcock fans?
Conclusions


• “Build it and they will come”
  − demand continues to grow
  − we are still limited by compute power & memory
• We need simpler tools for:
  − development of parallel applications
  − fault tolerance
• Connectivity to end users is important!
Acknowledgements


• I would like to thank
  −   bp for permission to give this presentation,
  − the organizers for inviting me to talk today,
  − you for listening.

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:1
posted:6/4/2012
language:
pages:25