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Parallel Considerations of VELO PatPixel Tracking Algorithms and Implementation

This article outlines the problem description, test setup, and integration with the Gaudi framework of the VELO PatPixel tracking system. It discusses the current test setup, algorithm precision in track generation, and parallelism considerations for better performance. The author, Daniel Hugo Cámpora Pérez, highlights the challenges and advantages of implementing parallel processing in pixel tracking algorithms for particle physics experiments.

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Parallel Considerations of VELO PatPixel Tracking Algorithms and Implementation

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  1. Parallel considerations of VELO PatPixelTracking Daniel Hugo Cámpora Pérez LHCb Online team

  2. Outline • PatPixel problem description • Test setup, some results • Integration with Gaudi framework Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  3. Outline • PatPixel problem description • Test setup, some results • Integration with Gaudi framework Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  4. Fast Pixel problem description Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  5. Fast Pixel problem description Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  6. Fast Pixel problem description • 48 sensors with 12 chips each • Each chip has 256x256 pixels • Clustered 2x2 by readout board • Right and left sensors at different z with overlap Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  7. Fast Pixel problem description • The algorithm searches for hits starting from the last pixel lattice. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  8. Fast Pixel problem description • The algorithm searches for hits starting from the last pixel lattice. • Per hit, it searches for compatible hits (on a given radius) in the next pixel lattice. • Finding at least three compatible hits forms a track. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  9. Fast Pixel problem description However, the current approach is very sequential (albeit efficient!). • Hits must not be already used. • Continue instructions, break the loop and make it fast. Porting the same algorithm to other programming models as is makes for a proof of concept (produced physics are the same). Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  10. Outline • PatPixel problem description • Test setup, some results • Integration with Gaudi framework Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  11. Current test setup Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  12. Current test setup We are interested in the search bit! Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  13. Current test setup • Input is 200 Monte-Carlo generated events. • Implementations produce exactly the same output as Brunel, unless stated otherwise. • Current setup runs TBB with a variable number of threads specified by task_scheduler_init init(i); • 1000 experiments are run per configuration. Results shown are the mean of those, standard deviation is checked as well. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  14. Comparing apples to… • Lab13 • Intel Xeon CPU E5-2650 (2 CPUs) • 20M Cache, 2.00 GHz (2.80 GHz TB) • 8 cores, 16 HW threads • Intel MIC (Pre-Production Intel® Xeon Phi™ coprocessors) • 1.1 GHz • 61 cores, 244 HW threads • GPU • NVIDIA GeForce 680GTX • 1GHz • 1536 CUDA cores (96 SIMD cores) Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  15. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  16. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  17. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  18. Precision • Is there a real need of double point operations? How about single precision instead… Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  19. Precision Mean 1 (correct / produced tracks): 100% Mean 2 (correct / total number of tracks): 99.9964% • We miss one track in 28.000. • No incorrect tracks are generated. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  20. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  21. Ma(g)ny-cores like Single Precision! Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  22. Current implementation • Setup is decoupled from the Gaudi framework. • Produced physics are the same. • Parallelism is setup as thread per event. • GPU acts as simple SIMD (“speedup” of 0.3x !) • divergent branches and warps are not good friends Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  23. Event-wise parallelism? Using a similar idea to the baselinealgorithm, we can exploit the inherent parallel nature of the problem. • Average #hits per sensor: 22.6 • Average multiplicity (hit x hit): 771.15 • Average multiplicity (hit x hit x hit): 1544.7 Early stage parallel algorithm produces 85% of the correct tracks. Different results doesn’t necessarily mean wrong!  Physics demonstration! Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  24. Outline • PatPixel problem description • Test setup, some results • Integration with Gaudi framework Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  25. What’s missing? The current setup is cool and dandy for comparing results, but not for testing the real setup! Daniel Hugo Cámpora Pérez 26-10-2012

  26. Integration with Gaudi Current HLT doesn’t consider having coprocessors to help in the execution of any step. Framework is sequential! Per event execution on a coprocessor is not realistic. Memory copies will kill us! • Each event is approximately 50kB. • Processing one single event is trivial. We have to hide the latency! Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  27. Pipelining! Eg. #event chunk = 200 Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  28. Integration with Gaudi Gaudihive Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  29. Integration with Gaudi Gaudihive Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

  30. In conclusion • The analysis on the sequential algorithm is complete. • A speedup of 10.70x has been obtained by properly configuring TBB. • MIC underperforms because of lack of use of VPUs, more tweaking is necessary. • Using floats rather than doubles is beneficial for many-core architectures, and results are the same. • A parallel version of the PatPixel would show a more realistic architecture comparison, and should be better performant. • The current framework with a good pipeline could enable the use of a coprocessor in a production environment. Daniel Hugo Cámpora Pérez - Parallel Considerations of VELO PatPixelTracking 21-11-2012

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