A Gomez TimTrack at C E S G A

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TimTrack is a software for tracking charged particles that uses C language for its speed and flexibility. It uses linear algebra libraries like LAPACK and Intel IPP for numerical computations like solving systems of linear equations. TimTrack version 2.0 implements a new algebra version for faster variance-covariance matrix calculations. Future versions will parallelize computations using OpenMP, MPI, and GPUs with CUDA to further improve performance for large particle datasets.

Technology Education

timTrack
tracking of charged particles

By J.A.Rodríguez

running ...

Datos.txt Detector.txt

timTrack

Resultados.txt
(output file)

- 6 SAETA (x,y,x',y',v,t)
- 6 Errors
-15 Covariances

Why C language ?
− Very fast
− Flexible
− Parallelism
− A rich set of libraries

Libraries was used to program timTrack (“algorithms ”)

LAPACK
Intel® IPP

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

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.

timTrack SAETAs solutions

PREVIOUS VERSION NEW algebra VERSION

timTrack v1.0 (LAPACK) timTrack v2.0 (LAPACK)
timTrack v1.1 (IPP)

timTrack variance-covariance matrix

PREVIOUS NEW algebra VERSION
VERSIONS

timTrack v2.0 (LAPACK)
timTrack v1.0 (LAPACK)
timTrack v1.1 (IPP)

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

:)

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

timTrack v2.1
Next step ( still in progress… )

Parallelims with Intel® MPI libraries
Shared parallelism with OpenMP for Multi-core

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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A Gomez TimTrack at C E S G A

1. timTrack tracking of charged particles By J.A.Rodríguez

2. TRASGO Project labCAF

3. RPC TRB timTrack

4. running ... Datos.txt Detector.txt timTrack Resultados.txt (output file) - 6 SAETA (x,y,x',y',v,t) - 6 Errors -15 Covariances

5. Why C language ? − Very fast − Flexible − Parallelism − A rich set of libraries Libraries was used to program timTrack (“algorithms ”) LAPACK Intel® IPP

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. 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. timTrack SAETAs solutions PREVIOUS VERSION NEW algebra VERSION timTrack v1.0 (LAPACK) timTrack v2.0 (LAPACK) timTrack v1.1 (IPP)

9. timTrack variance-covariance matrix PREVIOUS NEW algebra VERSION VERSIONS timTrack v2.0 (LAPACK) timTrack v1.0 (LAPACK) timTrack v1.1 (IPP)

10. Example Implemented X Z T1 T2 Y

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. 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. timTrack v2.1 Next step ( still in progress… ) Parallelims with Intel® MPI libraries Shared parallelism with OpenMP for Multi-core

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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