Flexible control of data transfer between parallel programs
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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

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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

Grid 2004


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


Roadmap

  • Motivation

  • Approximate Matching

  • Matching properties

  • Performance results

  • Conclusions and future work

Grid 2004


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

  • Appropriate tools

  • Coordinate transformation

  • Domain knowledge

Grid 2004


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


Ap0.Sr12

Ap1.Sr0

Ap0.Sr4

Ap2.Sr0

Ap0.Sr5

Ap4.Sr0

Separate codes from matching

Exporter Ap0

Configuration file

Importer Ap1

Grid 2004


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

  • Exporter Ap0 produces a sequence of data object A at simulation times 1.1, 1.2, 1.5, and 1.9

    • [email protected], [email protected], [email protected], [email protected]

  • Importer Ap1 requests the same data object A at time 1.3

    • [email protected]

  • Is there a match for [email protected]? If Yes, which one and why?

Grid 2004


Supported matching policies

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

  • LUBminimum f(x) with f(x) ≥ x

  • GLBmaximum f(x) with f(x) ≤ x

  • REGf(x) minimizes |f(x)-x| with |f(x)-x| ≤ p

  • REGUf(x) minimizes f(x)-x with 0 ≤ f(x)-x ≤ p

  • REGLf(x) minimizes x-f(x) with 0 ≤ x-f(x) ≤ p

  • FASTRany f(x) with |f(x)-x| ≤ p

  • FASTUany f(x) with 0 ≤ f(x)-x ≤ p

  • FASTLany f(x) with 0 ≤ x-f(x) ≤ p

Grid 2004


te’

te’’

Acceptable ≠ Matchable

Grid 2004


te’

Region-type matches

Grid 2004


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

Ap1 execution time (average)

Grid 2004


Experiment result 2

Ap1 pseudo code

Ap1 overhead in the slowest process

Grid 2004


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

    • 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


End of Talk


Main components

Grid 2004


Local and Remote requests

Grid 2004


Space Science Application

Grid 2004


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