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HiggsToTauTau analysis: parallel

Overview

Teaching: 10 min
Exercises: 20 min
Questions

  • Challenge: write the HiggsToTauTau analysis parallel workflow and run it on REANA

Objectives

  • Develop a full HigssToTauTau analysis workflow using parallel language

Overview

We have seen an example of a full DAG-aware workflow language called Yadage and how it can be used to describe and run the RooFit example and a simple version of HiggsToTauTau example.

In this episode we shall see how to efficiently apply parallelism to speed up the HiggsToTauTau example via the scatter-gather paradigm introduced in the previous episode.

HiggsToTauTau analysis

The overall reana.yaml looks like:

inputs:
  files:
    - steps.yaml
    - workflow.yaml
  parameters:
    files:
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/GluGluToHToTauTau.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/VBF_HToTauTau.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/DYJetsToLL.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/TTbar.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W1JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W2JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W3JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/Run2012B_TauPlusX.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/Run2012C_TauPlusX.root
    cross_sections:
      - 19.6
      - 1.55
      - 3503.7
      - 225.2
      - 6381.2
      - 2039.8
      - 612.5
      - 1.0
      - 1.0
    short_hands:
      - [ggH]
      - [qqH]
      - [ZLL,ZTT]
      - [TT]
      - [W1J]
      - [W2J]
      - [W3J]
      - [dataRunB]
      - [dataRunC]
workflow:
  type: yadage
  file: workflow.yaml
outputs:
  files:
    - fit/fit.png

Note that we define input files and cross sections and short names as an array. It is this array that we shall be scattering around.

HiggsToTauTau skimming

The skimming step definition looks like:

- name: skim
  dependencies: [init]
  scheduler:
    scheduler_type: multistep-stage
    parameters:
      input_file: {step: init, output: files}
      cross_section: {step: init, output: cross_sections}
      output_file: '{workdir}/skimmed.root'
    scatter:
       method: zip
       parameters: [input_file, cross_section]
    step: {$ref: 'steps.yaml#/skim'}

where the step is defined as:

skim:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      ./skim {input_file} {output_file} {cross_section} 11467.0 0.1
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      skimmed_file: '{output_file}'

Note the scatter paradigm that will cause nine parallel jobs for each input dataset file.

HiggsToTauTau histogramming

The histograms can be produced as follows:

- name: histogram
  dependencies: [skim]
  scheduler:
    scheduler_type: multistep-stage
    parameters:
      input_file: {stages: skim, output: skimmed_file}
      output_names: {step: init, output: short_hands}
      output_dir: '{workdir}'
    scatter:
       method: zip
       parameters: [input_file, output_names]
    step: {$ref: 'steps.yaml#/histogram'}

with:

histogram:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      for x in {output_names}; do
        python histograms.py {input_file} $x {output_dir}/$x.root;
      done
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    glob: true
    publish:
      histogram_file: '{output_dir}/*.root'

HiggsToTauTau merging

Gather time! How do we merge scattered results?

- name: merge
  dependencies: [histogram]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      input_files: {stages: histogram, output: histogram_file, flatten: true}
      output_file: '{workdir}/merged.root'
    step: {$ref: 'steps.yaml#/merge'}

with:

merge:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      hadd {output_file} {input_files}
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      merged_file: '{output_file}'

HiggsToTauTau fitting

The fit can be performed as follows:

- name: fit
  dependencies: [merge]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      histogram_file: {step: merge, output: merged_file}
      fit_outputs: '{workdir}'
    step: {$ref: 'steps.yaml#/fit'}

with:

fit:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      python fit.py {histogram_file} {fit_outputs}
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-statistics-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      fit_results: '{fit_outputs}/fit.png'

HiggsToTauTau plotting

Challenge time! Add plotting step to the workflow.

Exercise

Following the example above, write plotting step and plug it into the overall workflow.

Solution

The addition to the worklow specification is:

- name: plot
  dependencies: [merge]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      histogram_file: {step: merge, output: merged_file}
      plot_outputs: '{workdir}'
    step: {$ref: 'steps.yaml#/plot'}

The step is being defined as:

plot:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      python plot.py {histogram_file} {plot_outputs} 0.1
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      fitting_plot: '{plot_outputs}'

Full workflow

We are now ready to assemble the previous stages together and run the example on the REANA cloud.

Exercise

Write and run the HiggsToTauTau parallel workflow on REANA cloud. How many job does the workflow have? How much faster it is executed when compared to the simple Serial version?

Solution

The REANA specification file reana.yaml looks as follows:

inputs:
 files:
   - steps.yaml
   - workflow.yaml
  parameters:
    files:
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/GluGluToHToTauTau.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/VBF_HToTauTau.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/DYJetsToLL.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/TTbar.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W1JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W2JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/W3JetsToLNu.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/Run2012B_TauPlusX.root
      - root://eospublic.cern.ch//eos/root-eos/HiggsTauTauReduced/Run2012C_TauPlusX.root
    cross_sections:
      - 19.6
      - 1.55
      - 3503.7
      - 225.2
      - 6381.2
      - 2039.8
      - 612.5
      - 1.0
      - 1.0
    short_hands:
      - [ggH]
      - [qqH]
      - [ZLL,ZTT]
      - [TT]
      - [W1J]
      - [W2J]
      - [W3J]
      - [dataRunB]
      - [dataRunC]
workflow:
  type: yadage
  file: workflow.yaml
outputs:
  files:
    - fit/fit.png

The workflow definition file workflow.yaml is:

stages:
- name: skim
  dependencies: [init]
  scheduler:
    scheduler_type: multistep-stage
    parameters:
      input_file: {step: init, output: files}
      cross_section: {step: init, output: cross_sections}
      output_file: '{workdir}/skimmed.root'
    scatter:
       method: zip
       parameters: [input_file, cross_section]
    step: {$ref: 'steps.yaml#/skim'}

- name: histogram
  dependencies: [skim]
  scheduler:
    scheduler_type: multistep-stage
    parameters:
      input_file: {stages: skim, output: skimmed_file}
      output_names: {step: init, output: short_hands}
      output_dir: '{workdir}'
    scatter:
       method: zip
       parameters: [input_file, output_names]
    step: {$ref: 'steps.yaml#/histogram'}

- name: merge
  dependencies: [histogram]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      input_files: {stages: histogram, output: histogram_file, flatten: true}
      output_file: '{workdir}/merged.root'
    step: {$ref: 'steps.yaml#/merge'}

- name: fit
  dependencies: [merge]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      histogram_file: {step: merge, output: merged_file}
      fit_outputs: '{workdir}'
    step: {$ref: 'steps.yaml#/fit'}

- name: plot
  dependencies: [merge]
  scheduler:
    scheduler_type: singlestep-stage
    parameters:
      histogram_file: {step: merge, output: merged_file}
      plot_outputs: '{workdir}'
    step: {$ref: 'steps.yaml#/plot'}

The workflow steps defined in steps.yaml are:

skim:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      ./skim {input_file} {output_file} {cross_section} 11467.0 0.1
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      skimmed_file: '{output_file}'

histogram:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      for x in {output_names}; do
        python histograms.py {input_file} $x {output_dir}/$x.root;
      done
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    glob: true
    publish:
      histogram_file: '{output_dir}/*.root'

merge:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      hadd {output_file} {input_files}
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      merged_file: '{output_file}'

fit:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      python fit.py {histogram_file} {fit_outputs}
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-statistics-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      fit_results: '{fit_outputs}/fit.png'

plot:
  process:
    process_type: 'interpolated-script-cmd'
    script: |
      python plot.py {histogram_file} {plot_outputs} 0.1
  environment:
    environment_type: 'docker-encapsulated'
    image: gitlab-registry.cern.ch/awesome-workshop/awesome-analysis-eventselection-stage3
    imagetag: master
  publisher:
    publisher_type: interpolated-pub
    publish:
      fitting_plot: '{plot_outputs}'

Results

The computational graph of the workflow looks like:

The workflow produces the following fit:

Key Points

  • Use step dependencies to express main analysis stages

  • Use scatter-gather paradigm in staged to massively parallelise DAG workflow execution

  • REANA usage scenarios remain the same regardless of workflow language details

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