a gomez t tat cesga
TRANSCRIPT
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timTracktracking of charged particles
By J.A.Rodríguez
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TRASGO Project
labCAF
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RPC TRB timTrack
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timTrack
Resultados.txt(output file)
- 6 SAETA (x,y,x',y',v,t)- 6 Errors -15 Covariances
running ...
Datos.txt Detector.txt
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Why C language ? Very fast Flexible Parallelism A rich set of libraries
Libraries was used to program timTrack (“algorithms ”)
LAPACK
Intel® IPP
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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
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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.
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timTrack SAETAs solutions
PREVIOUS VERSION
timTrack v1.0 (LAPACK)
timTrack v1.1 (IPP)
NEW algebra VERSION
timTrack v2.0 (LAPACK)
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timTrack variance-covariance matrix
PREVIOUS VERSIONS
timTrack v1.0 (LAPACK)
timTrack v1.1 (IPP)
NEW algebra VERSION
timTrack v2.0 (LAPACK)
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Example Implemented
Z
Y
T1 T2
X
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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
:)
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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
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timTrack v2.1
Next step ( still in progress… )
Parallelims with Intel® MPI librariesShared parallelism with OpenMP for Multi-core
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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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