A license booster for engineers who want maximum throughput at minimum cost.
Ask us about Batch-Packs!
Call 1.800.763.7005 or 425.653.1200
Batch-mode is a term nearly as old as computers themselves. Despite its age, however, it is representative of a concept that is as relevant today as it ever was, perhaps even more so: headless (scripted, programmatic, automated, etc.) execution of instructions. Lots of engineering is done interactively, of course, but oftentimes the task is a known quantity and there is a ton of efficiency to be gained by automating the computational elements. That efficiency is realized ten times over when batch-mode meets parallelization – and that’s why we thought it was high-time we offered a batch-mode licensing model for Tecplot 360’s Python API, PyTecplot. We call them “batch-packs.”
Tecplot 360 Batch-Packs
Tecplot 360 batch-packs work by enabling users to run multiple concurrent instances of our Python API (PyTecplot) while consuming only a single license seat. It’s an optional upgrade that any customer can add to their license for a fee. The benefit? The fee for a batch-pack is substantially lower than buying an equivalent number of license seats – which makes it easier to justify outfitting your engineers with the software access they need to reach peak efficiency.
Here is a handy little diagram we drew to help explain it better:
Each network license allows ‘n’ seats. Traditionally, each instance of PyTecplot consumes 1 seat. Prior to the 2022 R2 release of Tecplot 360 EX, licenses only operated using the paradigm illustrated in the first two rows of the diagram above (that is, a user could check out up to ‘n’ seats, or ‘n’ users could check out a single seat). Now customers can elect to purchase batch-packs, which will enable each seat to provide a single user with access to ‘m’ instances of PyTecplot, as shown in the bottom row of the figure.
In addition to a cost reduction (vs. purchasing an equivalent number of network seats), batch-pack licensees will enjoy:
- Reduced license contention. Since each user is guaranteed “m” PyTecplot instances they can run post-processing jobs in parallel without fear of their job failing due to license contention.
- Faster turnaround times by running your post-processing jobs in parallel across multiple nodes of an HPC, or even on a single workstation. Running across multiple nodes may help alleviate memory limitations for large datasets.
We’re excited to offer this new option and hope that our customers can make the most of it.