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Parallel Cartographic Modeling

Parallel Cartographic Modeling. Kiumars Soltani 6th July 2012 . Outline. Rethinking Euclidean distance and direction calculations. The timing method Run the code on Trestles Profiling. Euclidean Direction and Distance. Sequential version was faster than the parallel version. Remember?

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Parallel Cartographic Modeling

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  1. Parallel Cartographic Modeling Kiumars Soltani 6th July 2012

  2. Outline • Rethinking Euclidean distance and direction calculations. • The timing method • Run the code on Trestles • Profiling

  3. Euclidean Direction and Distance • Sequential version was faster than the parallel version. Remember? • Some calculations are time consuming

  4. Euclidean Direction and Distance • There are others expression in the code(particularly Euclidean Distance & Direction calculation function) that can be improved in the same way. • This can be considered as a general approach during the development of the DSL: • We have to avoid redundant calculation • We have to use the fastest possible method to achieve the result.

  5. Change the Timing Method Using Linux’s own method of timing struct timeval tv1,tv2; double diff; gettimeofday(&tv1,NULL); // THE SECTION THAT WE WANT TO TIME gettimeofday(&tv2,NULL); diff = (tv2.tv_sec - tv1.tv_sec) * 1000.0; // sec to ms diff += (tv2.tv_usec - tv1.tv_usec) / 1000.0; // us to ms

  6. It’s all part of the input Change the way program get the input file and parameters ./model1.out INPUT_FILE #rows #columns #cores This way we can test the program with different input files and different number of available cores by running one script.

  7. Running the codes on Trestles • One node -> 32 cores • Using Intel C Compiler REMINDER Model 1 is solving the loop-carried dependency by defining critical sections. Model 2 is solving the loop-carried dependency by loop interchanging. The main part of the Model 2 is now using static scheduling with the chunk size 1.

  8. Check the Result Model 1 Model 2

  9. Running Time

  10. Profiling – Euclidean Distance & Direction

  11. Profiling – Euclidean Distance & Direction

  12. Profiling – Main Computation Part

  13. Profiling – Main Computation Part

  14. Future improvements • Improving the profiling approach • Rethinking the Euclidean distance and direction calculations method. • Summarizing the computations • Investigating the underlying reason that makes the parallel version not efficient. • Change the scheduling of the model 1.

  15. Thank You! Got Question?

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