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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.

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Flexible Control of Data Transfer between Parallel Programs

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  1. Flexible Control of Data Transfer between Parallel Programs Joe Shang-chieh Wu Alan Sussman Department of Computer Science University of Maryland, USA

  2. Particle and Hybrid model Corona and solar wind Rice convection model Global magnetospheric MHD Thermosphere-ionosphere model Grid 2004

  3. 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

  4. Roadmap • Motivation • Approximate Matching • Matching properties • Performance results • Conclusions and future work Grid 2004

  5. 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

  6. Solving multiple space scales • Appropriate tools • Coordinate transformation • Domain knowledge Grid 2004

  7. 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

  8. Ap0.Sr12 Ap1.Sr0 Ap0.Sr4 Ap2.Sr0 Ap0.Sr5 Ap4.Sr0 Separate codes from matching Exporter Ap0 Configuration file Importer Ap1 Grid 2004

  9. 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

  10. Approximate Matching • Exporter Ap0 produces a sequence of data object A at simulation times 1.1, 1.2, 1.5, and 1.9 • A@1.1, A@1.2, A@1.5, A@1.9 • Importer Ap1 requests the same data object A at time 1.3 • A@1.3 • Is there a match for A@1.3? If Yes, which one and why? Grid 2004

  11. 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

  12. te’ te’’ Acceptable ≠ Matchable Grid 2004

  13. te’ Region-type matches Grid 2004

  14. 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

  15. Experiment result 1 Ap1 execution time (average) Grid 2004

  16. Experiment result 2 Ap1 pseudo code Ap1 overhead in the slowest process Grid 2004

  17. 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

  18. 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

  19. End of Talk

  20. Main components Grid 2004

  21. Local and Remote requests Grid 2004

  22. Space Science Application Grid 2004

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