Hackathon GriCAD 2017

Jaime Arias, Pierre-Antoine Bouttier, Marcelo Forets

 

What is a Hackathon?

General principle

• As in a marathon, an intensive effort...

• ...to try to solve a problem...

• Implying several people (and a competition).

 

In real life

• A team (and no competition)...

• ...to try to solve a problem (generally in computer science)...

• ... with a limited amount of time.

 

And "our Hackathon"?

Originally, the exercice was proposed by GENCI.

 

Details

• Around each national or regional HPC center

• A team of students/researchers/engineers

• 2 days

• To transform/optimize a real numerical code to run it efficiently on HPC clusters

The Grenoble Team...

Jaime Arias (Research engineer, Mistis, Inria)

Marcelo Forets (Post-doc researcher, Tempo, Verimag)

Pierre-Antoine Bouttier (Research engineer, GriCAD)

And the subject.

• A proposition (among others) from L. Simula (Pr. at ENS Lyon)

• A code that helps to study what are the mechanisms to find an optimal income tax in the context of 2 countries playing Nash game.

Etienne Lehmann, Laurent Simula, Alain Trannoy; Tax me if you can! Optimal Nonlinear Income Tax Between Competing Governments, The Quarterly Journal of Economics, Volume 129, Issue 4

The code was written in the Mathematica language

 

...We were (and, in fact, also are) not specialists

of these scientific research fields!

What were our objectives?

• To rewrite the code in a more "HPC friendly" language...

 

• ...With the ulterior motive to make it easier to develop and run.

 

• To run it on a GriCAD HPC cluster and to see how we made an incredible work in improving greatly its performances (principally run time).

 

Obstacles : Time, unknown (for us) scientific context, symbolic to numerical computing.

The main lines of our work process

• Translation of the original code in Python

 

• Test and refactoring the new code

 

• Optimizing the performance (CPU time)

 

• Crossing the fingers (all along the process, in fact)

Spoiler

We have produced a Python code which:

 

• Gives closed numerical results than the original code

 

• Runs on the Froggy machine (a GriCAD HPC cluster)

 

• Exploits multiple cores (placed on a unique node)

 

• Goes 20 times faster than the original code

git clone https://gricad-gitlab.univ-grenoble-alpes.fr/bouttiep/hackathon2017.git

Focus

Now, we focus on some specific aspects of this exercise:

 

• What are the collaborative tools that we have used?

• What was our strategy about testing and optimizing our code?

• What have we learnt?

Collaborative Tools for Scientists

  • Write and execute code
  • Nice visualization capabilities
  • Real-time conversations for the team
  • Revision control system
  • Collaborative LaTeX edition
  • Responsive (no lags, etc.)
  • Comfortable for reading and reviewing code
  • Enhanced text edition (eg. Markdown, Sphinx)
  • Publishable results (private/public links)

Some desirable features:

Git and Gitlab

  • Most widespread revision control system
  • Gitlab: web interface for project management
  • "Issues" tracker
  • Local server is available (Univ. Grenoble Alpes)

Real-time collaborative LaTeX edition

Jupyter notebooks with chat embedded

CoCalc

(Collaborative Calcuation in the Cloud)

Linux terminal

Terminal

(how we actually launched our app)

  • Secure authentication (SSH), can be used outside the university
  • Steps:
  1. Connect to CIMENT
  2. Connect to the computing server (Froggy)
  3. Load the required modules (module avail)
  4. Run the computation (with oarsub)
  • See CIMENT's wiki for more information

Joblib, a python library to use parallel for loops using multiprocessing

  • Joblib: running Python functions as Python jobs on several cores

  • Aim: to provide tools to easily achieve better performance and productivity when working with long running jobs

  • Easy to use! pip installable; docs & examples easy to google

Joblib, usage and performances

Results in a 8

cores node (Froggy)

  • An "embarrasingly parallel" illustrative example:

Parallel code

Serial code

From Mathematica to Python

Mathematica code:

  • Symbolic computation
  • Nested loops
  • Sequential code
  • Recomputation of the same values, 0 functions
  • IO + plotting + computation operations mixed
  • Notebook
  • Lack of name convetion

Python Package

Python code:

  • Vectorized code (numpy)
  • Parallel code (joblib)
  • Parametric
  • Functions and modules
  • Python 2 and 3
  • Micro testing (variable's value)

Performance

# cores Mathematica Python
1 ~107 s ~14 s
4 - ~6 s

What have we learnt?

Short answer: a lot of things.

 

• Collaborative tools and real-time uses (CoCalc, git, python notebooks, markdown/latex editing)

• Symbolic to numerical computing

• Hardware and software architecture of a HPC cluster

 

What have we learnt?

Short answer: how people work

 

• We did not know each other

• We do not have the same scientific or technical interests

• We felt that our work was useful

• We had a good time

And now?

Maybe a good idea to renew and promote this kind of exercise

 

• Locally (in the Grenoble-Alpes university environment)

• Regularly (once a year?)

• To adress to all scientific and technical communities

To sum up ...

  • This exercise is an excellent framework to learn!
  • Develop technical skills
  • Develop communicational skills
  • Interdisciplinary
  • Promote the use of HPC CIMENT's infrastructure
  • Apply this activity with students of different levels
  • Interact with the team that proposed this project, further develop our initial Python implementation

... Perspectives

Thank you

Questions, remarks?

Hackathon GriCAD 2017

By pabouttier

Hackathon GriCAD 2017

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