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CoMPI: Enhancing MPI based applications performance and scalability using run-time compression.

CoMPI: Enhancing MPI based applications performance and scalability using run-time compression. Rosa Filgueira, David E.Singh, Alejandro Calderón and Jesús Carretero University Carlos III of Madrid. Summary. Problem description Main objectives CoMPI Study of compression algorithms.

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CoMPI: Enhancing MPI based applications performance and scalability using run-time compression.

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  1. CoMPI: Enhancing MPI based applications performance and scalability using run-time compression. Rosa Filgueira, David E.Singh, Alejandro Calderón and Jesús Carretero University Carlos III of Madrid.

  2. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Evaluation of CoMPI • Results • Conclusions

  3. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Evaluation of CoMPI • Results • Conclusions

  4. Problem description

  5. Main objectives (1/2) • Reduce the communication transfer time for MPI.

  6. Main objectives (2/2) • CoMPI: Optimization of MPI communications by using compression. • Compression in all MPI primitives. • Fit any MPI application. • Transparent to user. • Run-time compression. • Studding of compression algorithms. • Selecting the best algorithm based on message characteristics.

  7. Summary • Problem description • Main objectives • CoMPI • How we have integrated compression into MPI • Set of compression algorithms proposed • Study of compression algorithms. • Evaluation of CoMPI • Results • Conclusions

  8. MPICH architecture (1/2)

  9. MPICH architecture (2/2)

  10. Compression of MPI Messages (1/2)

  11. Compression of MPI Messages (2/2) • Header in the exchanged message to inform: • Compression used or not, algorithm and length. • All compression algorithms are included in a single Compression Library: • CoMPI can be easily updated . • New compression algorithms can be included .

  12. Set of compression algorithms proposed (1/2)

  13. Set of compression algorithms proposed (2/2)

  14. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Conclusion of compression study. • Evaluation of CoMPI • Results • Conclusions

  15. Study of compression algorithms (1/7) • To select the most appropriated algorithm for each datatype based on: • Buffer size. • Redundancy level. • To Increase the transmission speed by using compression depends on: • Number of bits sent. • Time required to compress. • Time required to decompress.

  16. Study of compression algorithms (2/7) • For each algorithm, datatype, buffer size and redundancy level we will study theComplexity and Compression ratio.

  17. Study of compression algorithms (3/7)

  18. Study of compression algorithms (4/7) • Integer dataset

  19. Study of compression algorithms (5/7) • Floating-point dataset

  20. Study of compression algorithms (6/7) • Double precision dataset WITHOUT pattern

  21. Study of compression algorithms (7/7) • Double precision WITH pattern: Data sequence  50001.0, 50003.0 , 50005.0 …

  22. Conclusion of compression study

  23. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Evaluation of CoMPI • Results • Conclusions

  24. Evaluation of CoMPI

  25. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Evaluation of CoMPI • Results • Real Applications • Benchmarks • Conclusions

  26. Results (1/5) • BISP3D: • Floating-point data. • Improves between x1.2 and x1.4 with LZO.

  27. Results (2/5) • PSRG: • Integer data. • Improves up to x2 with LZO.

  28. Results (3/5) • STEM-II: • Floating-point data. • Improves to x1.4 with LZO.

  29. Results (4/5) • IS : • Integer data. • Improves to x1.2 with LZO. • Rice obtains good results with 32 processes.

  30. Results (5/5) • LU: • Double precision. • No better performance. Only with 64 processes by using FPC we obtain a speedup of x1.1

  31. Summary • Problem description • Main objectives • CoMPI • Study of compression algorithms. • Evaluation of CoMPI • Results • Conclusions • Principal Conclusion . • On going.

  32. Principal conclusions (1/2) • New Compression library integrated into MPI using MPICH distribution CoMPI. • CoMPI includes five different compression algorithms and compress all MPI primitives. • Main characteristics: • Transparent for the users. • Fit any application without any change in it. • We have evaluated CoMPI using: • Synthetic traces. • Real applications.

  33. Principal conclusion (2/2) • The results of evaluations demonstrated that in most of the cases, the compression: • Reduce the overall execution time. • Enhance the scalability. • When compression is not appropriated: • Little performance degradation.

  34. On going (1/2)

  35. On going (2/2)

  36. Questions?

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