A Gomez TimTrack at C E S G A

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A Gomez TimTrack at C E S G A

  1. 1. timTrack tracking of charged particles By J.A.Rodríguez
  2. 2. TRASGO Project labCAF
  3. 3. RPC TRB timTrack
  4. 4. running ... Datos.txt Detector.txt timTrack Resultados.txt (output file) - 6 SAETA (x,y,x',y',v,t) - 6 Errors -15 Covariances
  5. 5. Why C language ? − Very fast − Flexible − Parallelism − A rich set of libraries Libraries was used to program timTrack (“algorithms ”) LAPACK Intel® IPP
  6. 6. BLAS /LAPACK Is a software library for numerical linear algebra. It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition. Specific versions for each CPU model provided by the vendors
  7. 7. Intel® IPP Integrated Performance Primitives (Intel® IPP) Is a library of multicore-ready, highly optimized software functions for digital media and data- processing applications. Intel IPP contains a rich set of matrix and vector operations for a wide variety of applications.
  8. 8. timTrack SAETAs solutions PREVIOUS VERSION NEW algebra VERSION timTrack v1.0 (LAPACK) timTrack v2.0 (LAPACK) timTrack v1.1 (IPP)
  9. 9. timTrack variance-covariance matrix PREVIOUS NEW algebra VERSION VERSIONS timTrack v2.0 (LAPACK) timTrack v1.0 (LAPACK) timTrack v1.1 (IPP)
  10. 10. Example Implemented X Z T1 T2 Y
  11. 11. Times for 1.000.000 particles Old Python and Matlab versions (only 500.000 particles) 165m 47.137 s timTrack v2.0 LAPACK 23.615 s timTrack v1.1 intel®IPP 23.495 s timTrack v1.0 LAPACK 31.188 s :)
  12. 12. Next Steps • Analyze systematic computing errors • Check single-precision version • Parallelize – Shared memory (OpenMP) – MPI (master-slave) – Full distributed • Implement in GPU • Study full problem
  13. 13. timTrack v2.1 Next step ( still in progress… ) Parallelims with Intel® MPI libraries Shared parallelism with OpenMP for Multi-core
  14. 14. Future ! timtrack v 3.0 CUDA parallel computing architecture in GPUs CUDA has several advantages over traditional general purpose computation on GPUs * Scattered reads * Shared memory * Faster downloads from the GPU * Full support for integer and bitwise operations

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