There's a new video in the Gallery page that does two amazing things: first, it shows how incredibly fast GPU parallelism is for big computations, showing how Manifold can load even 1280 cores to 97% use. Second, even more amazing, it shows how some of those seemingly-obscure Transforms, like Curvature, Mean, can be used in real-life to create very useful and visually appealing displays. :-)
I use LIDAR continually. Looking forward to trying Mean Curvature on some of our projects to see if the enhanced imagery helps to identify potential critical areas.
I used a much smaller LIDAR file than the one in the video. I have far fewer GPU cores and only four CPU cores. Using a radius of 5 instead of a radius of 3, on a LIDAR set encompassing approximately 806 hectares, M9 took slightly over 2 seconds to render the image. Tried the same trick using M8 and got an answer in 0.986 seconds. I was not able in the brief time I had to play with it achieve the same Autocontrast-style image created in M9. Still very impressive.
Release 8 loads everything memory resident, so for small jobs, 8 can sometimes go faster than 9. That's great, except that when it tops out things go slow and stay slow. 9 has a different system, so even very large jobs go fast, much faster than 8.
Here's an interesting idea: try doing what the first GPU video illustrates, and turn off GPU using the pragma directive
PRAGMA ('gpgpu' = 'none');
It might just run faster without GPU for a small job. :-)
Forgot to mention: details matter when analyzing timings, especially when comparing two very different systems like 8 and 9. For example, to make sense of timings you have to know the exact GPU card in use. Why? Here's an example:
8 will work with older generation, pre-Fermi GPU cards. 9 won't, as 9 is built around reasonably recent, Fermi or later GPU cards that can work with reasonably recent CUDA.
If you try doing a mean curvature in 8 with an older GPU card, 8 could use it, but if it is pre-Fermi 9 won't use it (you can tell by looking in the Help dialog for 9) and instead will execute the job CPU-parallel. CPU parallelism in 9 is quick enough that it can often look like the GPU is being used. A radius of 5 for anything other than small data probably would be too much computation to mistake CPU parallelism for GPGPU, but if the data is smaller that could be an effect.
-Esri Press; Map Use: Reading, Analysis, Interpretation 8 edition (September 19, 2016) review
-3D Analyst or Spatial Analyst
"Because my dad promised me" ( interstellar ) but blackhole don't exist
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