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Analyze Your Time-Dependent Data 6 Times Faster


Let’s face it, analyzing large amounts of data can take seemingly forever. Animating images to a movie takes an entire weekend to complete. Why can’t Tecplot just do it for me?

This Webinar will show you how the power of Python can take advantage of multi-process parallelism. Using the Tecplot 360 Python API, PyTecplot, you can get your results up to six times faster. We’ll show results of two datasets:

  1. Deep water asteroid strike. 476 timesteps, each containing ~27 million grid points (structured). 707 Gb on disk. Data courtesy Los Alamos National Lab (https://oceans11.lanl.gov/deepwaterimpact/).
  2. Flow around a cylinder: 1434 timesteps, each containing ~1.3 million grid points (structured), 4 Gb on disk, Plot3D format.

Webinar Outline

The Problem

  • Animating to a movie takes a long time and consumes too much RAM.
  • Why can’t Tecplot 360 just do it for me? Much of the data pipeline is inherently single-threaded.

The Solution

  • Minimize Memory Use (use if you’re ok with the time, but unhappy with the RAM consumption). Also consider changing $!FILECONFIG TEMPFILEPATH or updating your TEMP/TMP environment variable.
  • Script with PyTecplot.
  • Use Python multiprocessing.

The Results

  • Given enough CPUs, get your results 6x faster.
  • Here are a few Caveats.
    • Speed is hardware dependent.
    • Know your constraints – Disk I/O, CPU, RAM.
    • Need enough licenses.
    • Stylesheets need to work with the datasets.
  • Future work includes testing on a distributed HPC system.

Thanks for watching!

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