In order to measure the decompression performance, the acl_decompressor tool is used to extract the relevant metrics and a python script is used to parse them.
Here are the clips we measure on:
- The 42 regression clips from CMU database
- The 5 clips from the Matinee fight scene
Note that the data is not yet conveniently packaged.
Here are the platforms we measure on:
- Desktop: Intel i7-6850K @ 3.8 GHz
- Laptop: MacBook Pro mid 2014 @ 2.6 GHz
- Phone: Android Nexus 5X @ 1.8 GHz
- Tablet: iPad Pro 10.5 inch @ 2.39 GHz
We only show a few compilers and architectures to keep the graphs readable. The Visual Studio 2017 numbers are nearly identical to Visual Studio 2015 and are omitted for that reason. GCC 7 lands in between VS 2015 and Clang 5 and is omitted. Only VS 2015 is shown for x86 because it is generally a bit slower than the x64 variant for all compilers and it isn't commonly used in games.
Unless otherwise specified, the results are from release 1.1.0
The uniformly sampled algorithm offers consistent performance regardless of the playback direction. Shown here is the median performance of decompress_pose with a cold CPU cache for 3 clips with forward, backward, and random playback on the iPad.
| Clip Name | Forward | Backward | Random |
|---|---|---|---|
| 104_30 | 2.209 us | 2.208 us | 2.208 us |
| Trooper_1 | 4.084 us | 4.292 us | 4.125 us |
| Trooper_Main | 40.917 us | 41.625 us | 40.917 us |
As can be seen, the performance is consistent regardless of the playback direction.
This function decompresses a whole pose in one go. Shown here is forward playback with a cold CPU cache.
Here is the delta with the previous version:
This function decompresses a single bone. To generate the graphs, we measure the cost of decompressing a whole pose one bone at a time. Shown here is forward playback with a cold CPU cache.
Here is the delta with the previous version:
