timTracktracking of charged particles

By J.A.Rodríguez

TRASGO Project

labCAF

RPC TRB timTrack

running ...

timTrack

Datos.txt Detector.txt

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 ”)

LAPACKIntel® IPP

BLAS /LAPACK

Is a software library for numerical linear algebra.It provides routines for solving systems of linear equations and linear least squares, eigenvalueproblems, and singular value decomposition.Specific versions for each CPU modelprovided by the vendors

Intel® IPP

Integrated Performance Primitives (Intel® IPP)

Is a library of multicore-ready, highly optimizedsoftware 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

timTrack v1.0 (LAPACK)

timTrack v1.1 (IPP)

NEW algebra VERSION

timTrack v2.0 (LAPACK)

timTrack variance-covariance matrix

PREVIOUS VERSIONS

timTrack v1.0 (LAPACK)

timTrack v1.1 (IPP)

NEW algebra VERSION

timTrack v2.0 (LAPACK)

Example ImplementedX

Z

T1 T2Y

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 librariesShared parallelism with OpenMP for Multi-core

Future !

timtrack v 3.0

CUDA parallel computing architecture in GPUsCUDA 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