A Gomez T Tat Cesga

•Télécharger en tant que PPT, PDF•

0 j'aime•429 vues

timTrack tracking of charged particles By J.A.Rodríguez

TRAS G O Project lab CAF

[object Object]

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

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

BLAS /LAPACK ,[object Object],[object Object],[object Object]

Intel® IPP ,[object Object],[object Object],[object Object]

timTrack SAETAs solutions ,[object Object],[object Object],[object Object],[object Object],[object Object]

timTrack variance-covariance matrix ,[object Object],[object Object],[object Object],[object Object],[object Object]

Example Implemented Z Y T1 T2 X

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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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

Future ! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommandé

A Gomez TimTrack at C E S G A

A Gomez TimTrack at C E S G A

A Gomez TimTrack at C E S G AMiguel Morales

MulticoreBirgit Plötzeneder

Intro to Machine Learning - Hierarchical Temporal Memory

Intro to Machine Learning - Hierarchical Temporal Memory

Intro to Machine Learning - Hierarchical Temporal MemoryMartin Chang

Programming Trends in High Performance Computing

Programming Trends in High Performance Computing

Programming Trends in High Performance ComputingJuris Vencels

The Search for Gravitational Waves

The Search for Gravitational Waves

The Search for Gravitational Wavesinside-BigData.com

Parallel Programming on the ANDC cluster

Parallel Programming on the ANDC cluster

Parallel Programming on the ANDC clusterSudhang Shankar

Code GPU with CUDA - Device code optimization principle

Code GPU with CUDA - Device code optimization principle

Code GPU with CUDA - Device code optimization principleMarina Kolpakova

Arm tools and roadmap for SVE compiler support

Arm tools and roadmap for SVE compiler support

Arm tools and roadmap for SVE compiler supportLinaro

Recommandé

A Gomez TimTrack at C E S G A

A Gomez TimTrack at C E S G A

A Gomez TimTrack at C E S G AMiguel Morales

MulticoreBirgit Plötzeneder

Intro to Machine Learning - Hierarchical Temporal Memory

Intro to Machine Learning - Hierarchical Temporal Memory

Intro to Machine Learning - Hierarchical Temporal MemoryMartin Chang

Programming Trends in High Performance Computing

Programming Trends in High Performance Computing

Programming Trends in High Performance ComputingJuris Vencels

The Search for Gravitational Waves

The Search for Gravitational Waves

The Search for Gravitational Wavesinside-BigData.com

Parallel Programming on the ANDC cluster

Parallel Programming on the ANDC cluster

Parallel Programming on the ANDC clusterSudhang Shankar

Code GPU with CUDA - Device code optimization principle

Code GPU with CUDA - Device code optimization principle

Code GPU with CUDA - Device code optimization principleMarina Kolpakova

Arm tools and roadmap for SVE compiler support

Arm tools and roadmap for SVE compiler support

Arm tools and roadmap for SVE compiler supportLinaro

resume-XinyuSui

resume-XinyuSui

resume-XinyuSuiXinyu Sui

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...Marina Kolpakova

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...Intel® Software

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUsPandey_G

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)Igalia

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...NECST Lab @ Politecnico di Milano

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & ProductivityLinaro

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loopsLinaro

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...eXascale Infolab

Python Basis Tutorial

Python Basis Tutorial

Python Basis Tutorialmd sathees

Python programming lab 19

Python programming lab 19

Python programming lab 19profbnk

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KITScilab

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the CompilerMarina Kolpakova

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)elliando dias

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001Casiano Rodriguez-leon

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001Casiano Rodriguez-leon

01. HAMS - Introduction

01. HAMS - Introduction

01. HAMS - IntroductionHAMSproject

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big ComputingJonathan Dursi

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North CarolinaAnton Bezuglov

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or FoeSAP HANA Cloud Platform

M Morales Sealed Rpcs

M Morales Sealed Rpcs

M Morales Sealed RpcsMiguel Morales

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

C Sanchez Reduction SaetasMiguel Morales

Contenu connexe

Tendances

resume-XinyuSui

resume-XinyuSui

resume-XinyuSuiXinyu Sui

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...Marina Kolpakova

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...Intel® Software

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUsPandey_G

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)Igalia

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...NECST Lab @ Politecnico di Milano

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & ProductivityLinaro

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loopsLinaro

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...eXascale Infolab

Python Basis Tutorial

Python Basis Tutorial

Python Basis Tutorialmd sathees

Python programming lab 19

Python programming lab 19

Python programming lab 19profbnk

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KITScilab

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the CompilerMarina Kolpakova

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)elliando dias

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001Casiano Rodriguez-leon

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001Casiano Rodriguez-leon

01. HAMS - Introduction

01. HAMS - Introduction

01. HAMS - IntroductionHAMSproject

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big ComputingJonathan Dursi

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North CarolinaAnton Bezuglov

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or FoeSAP HANA Cloud Platform

Tendances (20)

resume-XinyuSui

resume-XinyuSui

resume-XinyuSui

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...

Pragmatic optimization in modern programming - modern computer architecture c...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

D. Fast, Simple User-Space Network Functions with Snabb (RIPE 77)

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

OXiGen: Automated FPGA design flow from C applications to dataflow kernels - ...

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & Productivity

Programming Languages & Tools for Higher Performance & Productivity

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loops

An evaluation of LLVM compiler for SVE with fairly complicated loops

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

It Takes Two: Instrumenting the Interaction between In-Memory Databases and S...

Python Basis Tutorial

Python Basis Tutorial

Python Basis Tutorial

Python programming lab 19

Python programming lab 19

Python programming lab 19

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KIT

ScilabTEC 2015 - KIT

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Pragmatic Optimization in Modern Programming - Demystifying the Compiler

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)

Learning Erlang (from a Prolog dropout's perspective)

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

PREDICTING THE TIME OF OBLIVIOUS PROGRAMS. Euromicro 2001

01. HAMS - Introduction

01. HAMS - Introduction

01. HAMS - Introduction

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

EuroMPI 2016 Keynote: How Can MPI Fit Into Today's Big Computing

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North Carolina

Artificial Neural Networks for Storm Surge Prediction in North Carolina

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or Foe

[BGOUG] Java GC - Friend or Foe

En vedette

M Morales Sealed Rpcs

M Morales Sealed Rpcs

M Morales Sealed RpcsMiguel Morales

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

C Sanchez Reduction SaetasMiguel Morales

D Gonzalez Diaz Optimization Mstip Rp Cs

D Gonzalez Diaz Optimization Mstip Rp Cs

D Gonzalez Diaz Optimization Mstip Rp CsMiguel Morales

Jag Tim Track Gsi 20 Nov09 Short

Jag Tim Track Gsi 20 Nov09 Short

Jag Tim Track Gsi 20 Nov09 ShortMiguel Morales

M Morales Sealed Rpcs

M Morales Sealed Rpcs

M Morales Sealed RpcsMiguel Morales

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

G Rodriguez Tank CalibrationMiguel Morales

RR Osorio FPGAMiguel Morales

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

Jj Taboada C Rays ClimateMiguel Morales

En vedette (8)

M Morales Sealed Rpcs

M Morales Sealed Rpcs

M Morales Sealed Rpcs

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

D Gonzalez Diaz Optimization Mstip Rp Cs

D Gonzalez Diaz Optimization Mstip Rp Cs

D Gonzalez Diaz Optimization Mstip Rp Cs

Jag Tim Track Gsi 20 Nov09 Short

Jag Tim Track Gsi 20 Nov09 Short

Jag Tim Track Gsi 20 Nov09 Short

M Morales Sealed Rpcs

M Morales Sealed Rpcs

M Morales Sealed Rpcs

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

RR Osorio FPGA

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

Similaire à A Gomez T Tat Cesga

Porting a Streaming Pipeline from Scala to Rust

Porting a Streaming Pipeline from Scala to Rust

Porting a Streaming Pipeline from Scala to RustEvan Chan

Scale up and Scale Out Anaconda and PyData

Scale up and Scale Out Anaconda and PyData

Scale up and Scale Out Anaconda and PyDataTravis Oliphant

design-compiler.pdf

design-compiler.pdf

design-compiler.pdfFrangoCamila

Hardware & Software Platforms for HPC, AI and ML

Hardware & Software Platforms for HPC, AI and ML

Hardware & Software Platforms for HPC, AI and MLinside-BigData.com

Strata Singapore: GearpumpReal time DAG-Processing with Akka at Scale

Strata Singapore: GearpumpReal time DAG-Processing with Akka at Scale

Strata Singapore: GearpumpReal time DAG-Processing with Akka at ScaleSean Zhong

Cray XT Porting, Scaling, and Optimization Best Practices

Cray XT Porting, Scaling, and Optimization Best Practices

Cray XT Porting, Scaling, and Optimization Best PracticesJeff Larkin

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...Rodrigo Senra

emips_overview_apr08

emips_overview_apr08

emips_overview_apr08Neil Pittman

Using Parallel Computing Platform - NHDNUG

Using Parallel Computing Platform - NHDNUG

Using Parallel Computing Platform - NHDNUGNorth Houston .NET Users Group

Unmanaged Parallelization via P/Invoke

Unmanaged Parallelization via P/Invoke

Unmanaged Parallelization via P/InvokeDmitri Nesteruk

Scalability for All: Unreal Engine* 4 with Intel

Scalability for All: Unreal Engine* 4 with Intel

Scalability for All: Unreal Engine* 4 with Intel Intel® Software

Chicago Flink Meetup: Flink's streaming architecture

Chicago Flink Meetup: Flink's streaming architecture

Chicago Flink Meetup: Flink's streaming architectureRobert Metzger

Real-time Programming in Java

Real-time Programming in Java

Real-time Programming in JavaAleš Plšek

Lecture 4: Deep Learning Frameworks

Lecture 4: Deep Learning Frameworks

Lecture 4: Deep Learning FrameworksMohamed Loey

SequenceL Auto-Parallelizing Toolset Intro slideshare

SequenceL Auto-Parallelizing Toolset Intro slideshare

SequenceL Auto-Parallelizing Toolset Intro slideshareDoug Norton

SequenceL intro slideshare

SequenceL intro slideshare

SequenceL intro slideshareDoug Norton

LibOS as a regression test framework for Linux networking #netdev1.1

LibOS as a regression test framework for Linux networking #netdev1.1

LibOS as a regression test framework for Linux networking #netdev1.1Hajime Tazaki

Cooperative Task Execution for Apache Spark

Cooperative Task Execution for Apache Spark

Cooperative Task Execution for Apache SparkDatabricks

Linux and Open Source in Math, Science and Engineering

Linux and Open Source in Math, Science and Engineering

Linux and Open Source in Math, Science and EngineeringPDE1D

20081114 Friday Food iLabt Bart Joris

20081114 Friday Food iLabt Bart Joris

20081114 Friday Food iLabt Bart Jorisimec.archive

Similaire à A Gomez T Tat Cesga (20)

Porting a Streaming Pipeline from Scala to Rust

Porting a Streaming Pipeline from Scala to Rust

Porting a Streaming Pipeline from Scala to Rust

Scale up and Scale Out Anaconda and PyData

Scale up and Scale Out Anaconda and PyData

Scale up and Scale Out Anaconda and PyData

design-compiler.pdf

design-compiler.pdf

design-compiler.pdf

Hardware & Software Platforms for HPC, AI and ML

Hardware & Software Platforms for HPC, AI and ML

Hardware & Software Platforms for HPC, AI and ML

Strata Singapore: GearpumpReal time DAG-Processing with Akka at Scale

Strata Singapore: GearpumpReal time DAG-Processing with Akka at Scale

Strata Singapore: GearpumpReal time DAG-Processing with Akka at Scale

Cray XT Porting, Scaling, and Optimization Best Practices

Cray XT Porting, Scaling, and Optimization Best Practices

Cray XT Porting, Scaling, and Optimization Best Practices

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...

Python Brasil 2010 - Potter vs Voldemort - Lições ofidiglotas da prática Pyth...

emips_overview_apr08

emips_overview_apr08

emips_overview_apr08

Using Parallel Computing Platform - NHDNUG

Using Parallel Computing Platform - NHDNUG

Using Parallel Computing Platform - NHDNUG

Unmanaged Parallelization via P/Invoke

Unmanaged Parallelization via P/Invoke

Unmanaged Parallelization via P/Invoke

Scalability for All: Unreal Engine* 4 with Intel

Scalability for All: Unreal Engine* 4 with Intel

Scalability for All: Unreal Engine* 4 with Intel

Chicago Flink Meetup: Flink's streaming architecture

Chicago Flink Meetup: Flink's streaming architecture

Chicago Flink Meetup: Flink's streaming architecture

Real-time Programming in Java

Real-time Programming in Java

Real-time Programming in Java

Lecture 4: Deep Learning Frameworks

Lecture 4: Deep Learning Frameworks

Lecture 4: Deep Learning Frameworks

SequenceL Auto-Parallelizing Toolset Intro slideshare

SequenceL Auto-Parallelizing Toolset Intro slideshare

SequenceL Auto-Parallelizing Toolset Intro slideshare

SequenceL intro slideshare

SequenceL intro slideshare

SequenceL intro slideshare

LibOS as a regression test framework for Linux networking #netdev1.1

LibOS as a regression test framework for Linux networking #netdev1.1

LibOS as a regression test framework for Linux networking #netdev1.1

Cooperative Task Execution for Apache Spark

Cooperative Task Execution for Apache Spark

Cooperative Task Execution for Apache Spark

Linux and Open Source in Math, Science and Engineering

Linux and Open Source in Math, Science and Engineering

Linux and Open Source in Math, Science and Engineering

20081114 Friday Food iLabt Bart Joris

20081114 Friday Food iLabt Bart Joris

20081114 Friday Food iLabt Bart Joris

Plus de Miguel Morales

T Kurtukian Midas

T Kurtukian Midas

T Kurtukian MidasMiguel Morales

G Kornakov E A Smultivariate Analysis

G Kornakov E A Smultivariate Analysis

G Kornakov E A Smultivariate AnalysisMiguel Morales

J A Garzon Trasgo2010 Intro

J A Garzon Trasgo2010 Intro

J A Garzon Trasgo2010 IntroMiguel Morales

D Gonzalez Diaz Optimization Mstip R P Cs

D Gonzalez Diaz Optimization Mstip R P Cs

D Gonzalez Diaz Optimization Mstip R P CsMiguel Morales

J A Garzon Tim Trackfor Trasgos

J A Garzon Tim Trackfor Trasgos

J A Garzon Tim Trackfor TrasgosMiguel Morales

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

G Rodriguez Tank CalibrationMiguel Morales

R Vazquez Showers Signatures

R Vazquez Showers Signatures

R Vazquez Showers SignaturesMiguel Morales

P Cabanelas Hades Telescope

P Cabanelas Hades Telescope

P Cabanelas Hades TelescopeMiguel Morales

P Fonte Trasgo 2010

P Fonte Trasgo 2010

P Fonte Trasgo 2010Miguel Morales

D Belver FEE for Trasgos

D Belver FEE for Trasgos

D Belver FEE for TrasgosMiguel Morales

M Traxler TRB and Trasgo

M Traxler TRB and Trasgo

M Traxler TRB and TrasgoMiguel Morales

Ja Garzon Tim Trackfor Trasgos

Ja Garzon Tim Trackfor Trasgos

Ja Garzon Tim Trackfor TrasgosMiguel Morales

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

Jj Taboada C Rays ClimateMiguel Morales

G Kornakov Ea Smultivariate Analysis

G Kornakov Ea Smultivariate Analysis

G Kornakov Ea Smultivariate AnalysisMiguel Morales

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

C Sanchez Reduction SaetasMiguel Morales

Jag Trasgo Ucm090522

Jag Trasgo Ucm090522

Jag Trasgo Ucm090522Miguel Morales

Jag Timtrack F Matematicas 1 Dic09 Short

Jag Timtrack F Matematicas 1 Dic09 Short

Jag Timtrack F Matematicas 1 Dic09 ShortMiguel Morales

Jag Trasgo Lip081113

Jag Trasgo Lip081113

Jag Trasgo Lip081113Miguel Morales

Jag Trasgo Helsinki091002

Jag Trasgo Helsinki091002

Jag Trasgo Helsinki091002Miguel Morales

Jag Trasgo Ep Ferrol090930 Short

Jag Trasgo Ep Ferrol090930 Short

Jag Trasgo Ep Ferrol090930 ShortMiguel Morales

Plus de Miguel Morales (20)

T Kurtukian Midas

T Kurtukian Midas

T Kurtukian Midas

G Kornakov E A Smultivariate Analysis

G Kornakov E A Smultivariate Analysis

G Kornakov E A Smultivariate Analysis

J A Garzon Trasgo2010 Intro

J A Garzon Trasgo2010 Intro

J A Garzon Trasgo2010 Intro

D Gonzalez Diaz Optimization Mstip R P Cs

D Gonzalez Diaz Optimization Mstip R P Cs

D Gonzalez Diaz Optimization Mstip R P Cs

J A Garzon Tim Trackfor Trasgos

J A Garzon Tim Trackfor Trasgos

J A Garzon Tim Trackfor Trasgos

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

G Rodriguez Tank Calibration

R Vazquez Showers Signatures

R Vazquez Showers Signatures

R Vazquez Showers Signatures

P Cabanelas Hades Telescope

P Cabanelas Hades Telescope

P Cabanelas Hades Telescope

P Fonte Trasgo 2010

P Fonte Trasgo 2010

P Fonte Trasgo 2010

D Belver FEE for Trasgos

D Belver FEE for Trasgos

D Belver FEE for Trasgos

M Traxler TRB and Trasgo

M Traxler TRB and Trasgo

M Traxler TRB and Trasgo

Ja Garzon Tim Trackfor Trasgos

Ja Garzon Tim Trackfor Trasgos

Ja Garzon Tim Trackfor Trasgos

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

Jj Taboada C Rays Climate

G Kornakov Ea Smultivariate Analysis

G Kornakov Ea Smultivariate Analysis

G Kornakov Ea Smultivariate Analysis

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

C Sanchez Reduction Saetas

Jag Trasgo Ucm090522

Jag Trasgo Ucm090522

Jag Trasgo Ucm090522

Jag Timtrack F Matematicas 1 Dic09 Short

Jag Timtrack F Matematicas 1 Dic09 Short

Jag Timtrack F Matematicas 1 Dic09 Short

Jag Trasgo Lip081113

Jag Trasgo Lip081113

Jag Trasgo Lip081113

Jag Trasgo Helsinki091002

Jag Trasgo Helsinki091002

Jag Trasgo Helsinki091002

Jag Trasgo Ep Ferrol090930 Short

Jag Trasgo Ep Ferrol090930 Short

Jag Trasgo Ep Ferrol090930 Short

A Gomez T Tat Cesga

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

2. TRAS G O Project lab CAF

3.

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

5.

6.

7.

8.

9.

10. Example Implemented Z Y T1 T2 X

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.

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

14.

15.