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Flexible Control of Data Transfer between Parallel Programs. Joe Shang-chieh Wu Alan Sussman Department of Computer Science University of Maryland, USA. Particle and Hybrid model. Corona and solar wind. Rice convection model. Global magnetospheric MHD. Thermosphere-ionosphere model.

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flexible control of data transfer between parallel programs

Flexible Control of Data Transfer between Parallel Programs

Joe Shang-chieh Wu

Alan Sussman

Department of Computer Science

University of Maryland, USA


Particle and Hybrid model

Corona and solar wind

Rice convection model

Global magnetospheric MHD

Thermosphere-ionosphere model

Grid 2004

what is the problem
What is the problem?
  • Coupling existing (parallel) programs
    • for physical simulations more accurate answers can be obtained
    • for visualization, flexible transmission of data between simulation and visualization codes
  • Exchange data across shared or overlapped regions in multiple parallel programs
  • Couple multi-scale (space & time) programs
  • Focus on multiple time scale problems (when to exchange data)

Grid 2004

  • Motivation
  • Approximate Matching
  • Matching properties
  • Performance results
  • Conclusions and future work

Grid 2004

is it important
Is it important?
  • Petroleum reservoir simulations – multi-scale, multi-resolution code
  • Special issue in May/Jun 2004 of IEEE Computing in Science & Engineering

“It’s then possible to couple several existing calculations together through an interface and obtain accurate answers.”

  • Earth System Modeling Framework

several US federal agencies and universities. (http://www.esmf.ucar.edu)

Grid 2004

solving multiple space scales
Solving multiple space scales
  • Appropriate tools
  • Coordinate transformation
  • Domain knowledge

Grid 2004

matching is outside components
Matching is OUTSIDE components
  • Separate matching (coupling) information from the participating components
    • Maintainability – Components can be developed/upgraded individually
    • Flexibility – Change participants/components easily
    • Functionality – Support variable-sized time interval numerical algorithms or visualizations
  • Matching information is specified separately by application integrator
  • Runtime match via simulation time stamps

Grid 2004

separate codes from matching







Separate codes from matching

Exporter Ap0

Configuration file

Importer Ap1

Grid 2004

matching implementation
Matching implementation
  • Library is implemented with POSIX threads
  • Each process in each program uses library threads to exchange control information in the background, while applications are computing in the foreground
  • One process in each parallel program runs an extra representative thread to exchange control information between parallel programs
    • Minimize communication between parallel programs
    • Keep collective correctness in each parallel program
    • Improve overall performance

Grid 2004

approximate matching
Approximate Matching

Grid 2004

supported matching policies
Supported matching policies

<importer request, exporter matched, desired precision> = <x, f(x), p>

  • LUB minimum f(x) with f(x) ≥ x
  • GLB maximum f(x) with f(x) ≤ x
  • REG f(x) minimizes |f(x)-x| with |f(x)-x| ≤ p
  • REGU f(x) minimizes f(x)-x with 0 ≤ f(x)-x ≤ p
  • REGL f(x) minimizes x-f(x) with 0 ≤ x-f(x) ≤ p
  • FASTR any f(x) with |f(x)-x| ≤ p
  • FASTU any f(x) with 0 ≤ f(x)-x ≤ p
  • FASTL any f(x) with 0 ≤ x-f(x) ≤ p

Grid 2004

experimental setup
Experimental setup

Question : How much overhead introduced by runtime matching?

  • 6 PIII-600 processors, connected by channel-bonded Fast Ethernet
  • utt = uxx + uyy + f(t,x,y), solve 2-d diffusion equation by the finite element method.
  • u(t,x,y) : 512x512 array, on 4 processors (Ap1)
  • f(t,x,y) : 32x512 array, on 2 processors (Ap2)
  • All data in Ap2 is sent (exported) to Ap1 using matching criterion <REGL,0.05>
  • Ap1 receives (imports) data with 3 different scenarios. 1001 matches made for each scenario (results averaged over multiple runs)

Grid 2004

experiment result 1
Experiment result 1

Ap1 execution time (average)

Grid 2004

experiment result 2
Experiment result 2

Ap1 pseudo code

Ap1 overhead in the slowest process

Grid 2004

experiment result 3
Experiment result 3
  • Fastest process (P11)
    • - high cost, remote match
  • Slowest process (P13)
    • - low cost, local match
  • High cost match can be hidden

Comparison of matching time

Grid 2004

conclusions future work
Conclusions & Future work
  • Conclusions
    • Low overhead approach for flexible data exchange between different time scale e-Science components
  • Ongoing & future work
    • Performance experiments in Grid environment
    • Caching strategies to efficiently deal with slow importers
    • Real applications – space weather is the first one

Grid 2004