1. Parallel Cartographic Modeling1
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Parallel Cartographic Modeling
Kiumars Soltani
20th July 2012

2. Outline What is the project about? Define the goal of this project2
What is the project about?
Define the goal of this project
Is it similar to projects in the past or is it a new effort?
Define the scope of this project
Is it an independent project or is it related to other projects?
* Note that this slide is not necessary for weekly status meetings
Outline
Debugging the Code
Model 1
Model 2
Future Challenges

3. * If any of these issues caused a schedule delay or need to be discussed further, include details in next slide. 3
Debugging the Code
A mistake in indexing the DirectionRaster was giving us wrong result!
Count = … //offset
For(j=0; j < 600; j++) { /*
Calculating DirectionRaster */
DirectionRaster[count] = … }
DirectionRaster[count+j] = …

4. 4
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The results
The result from the previous version that was presented in the last presentation was for the 900k dataset, not 90k dataset.
Therefore, that result should have been compared to our result for the largest dataset.
That might lead to dramatic difference in the final efficiency as we have issues in scalability.
All the result are captured using 32 cores on Trestles.

5. 5
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Model 1
Taking advantage of locality
Store the local result of each core in a privately defined array.
Do the global sum on the local_result arrays.
Using static scheduling with the chunk size of 1.
Using nowait clause on the main loop

6. Model 1 – Result for 90k dataset6
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Model 1 – Result for 90k dataset
Reading File
Euclidean Direction and Distance
Computation
Writing Output
Old Version
0.620
5.05 1
Version 2.1
0.234
0.018
72.258
0.402

7. Model 1 – Result for 900k dataset7
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Model 1 – Result for 900k dataset
Reading File
Euclidean Direction and Distance
Computation
Writing Output
Old Version
7.140
5.120
1.060
Version 2.1
2.434
0.034
0.465

8. Profiling – Result for 900k dataset

9. Result for 900k dataset

10. 10
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Model 2
Was considerably faster for smaller values.
Doesn’t scale well!
The initial inspection shows that the most time consuming part is writing the output.
Writing the output in this model is much less than the model 1!
Using static scheduling with the chunk size of 1.
Using nowait clause on the main loop

11. Model 2 – Result for 90k dataset11
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Model 2 – Result for 90k dataset
Reading File
Euclidean Direction and Distance
Computation
Writing Output
Old Version
0.62
5.05 1
Version 2.1
0.234
0.030
91.350
0.392

12. Model 2 – Result for 900k dataset12
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Model 2 – Result for 900k dataset
Reading File
Euclidean Direction and Distance
Computation
Writing Output
Old Version
7.140
5.120
1.060
Version 2.1
2.327
0.022

13. Profiling – Result for 900k dataset

14. Future works Running the code for the dataset provided by April.
She is interested in investigating if the 12 hours period data for a single day produce similar result.
Doing more advanced profiling (with Babak Behzad)
Profiling the I/O related issues for Model 2(Cache miss, … )
This code’s improvement is basically done.
What we are expecting to get out of this implementation.
Domain Specific Language
CUDA, MPI, …

15. 15
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Thank You!
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