# Next ITK October Performance Confab

**URL:** https://discourse.itk.org/t/next-itk-october-performance-confab/268
**Category:** Community
**Tags:** performance
**Created:** [October 5, 2017, 8:07pm UTC](https://discourse.itk.org/t/next-itk-october-performance-confab/268 "2017-10-05T20:07:28Z")
**Posts on this page:** 2
**Page:** 1

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### Author: ![matt.mccormick](https://discourse.itk.org/user_avatar/discourse.itk.org/matt.mccormick/32/7_2.png) [@matt.mccormick](https://discourse.itk.org/u/matt.mccormick)
#### Post date: [October 5, 2017, 8:07pm UTC](https://discourse.itk.org/t/next-itk-october-performance-confab/268/1 "2017-10-05T20:07:31Z")

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Hi folks,

Let’s get together again to discuss progress on the ITK Performance efforts.

When: Friday, October 20th, 11 AM EST  
Where: [Google Meet https://meet.google.com/nue-tugx-pxt](https://meet.google.com/nue-tugx-pxt)  
What: Discuss ITK Performance progress

Notes from the previous discussion on 2017-07-28 can be found in [the weekly confab document](https://docs.google.com/document/d/1I6DHtiGsA5sIPxr9ae683j-vyBpdfFeUpaLWyEVcSSg/edit). We can also add notes there.

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### Author: ![blowekamp](https://discourse.itk.org/user_avatar/discourse.itk.org/blowekamp/32/79_2.png) [@blowekamp](https://discourse.itk.org/u/blowekamp)
#### Post date: [October 20, 2017, 12:59pm UTC](https://discourse.itk.org/t/next-itk-october-performance-confab/268/2 "2017-10-20T12:59:37Z")

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There was an interesting conversation held on the ITK Community mailing list regarding the time it takes to initialized the image registration framework:  
[https://public.kitware.com/pipermail/community/2017-September/013662.html](https://public.kitware.com/pipermail/community/2017-September/013662.html)

The conclusion was that a significant amount of time is consumed by the slow DiscreteGuassianImageFilter, the SmoothingRecursiveGaussianImageFilter can provide improved performance, but slightly ( insignificant? ) different results.

I would also like to note, that when a gaussian filter, then a “shrink” filter is done, more efficiency can be gained by doing the gaussian in 1D, then shrink in 1D, repeat. Consider a case where you are shrinking by 10x10x10, This amount of work done by the gaussian can be reduced by over 100x.