Short answer: Slower than CUDA’s native types (float3, float4 etc). In the course of my real time graphics project I’ll extend and replace NVIDIA’s proprietary HelperMath header to include matrix multiplications and other helper functions I need. After the project is finished I’ll probably publish this library as open source (naturally not including any of NVIDIA’s code).
I’m working on a little ray tracing CUDA project right now and found out, that GLM also works in that environment. But soon I ran into performance issues and was looking for the culprit. While it certainly isn’t only GLM, it still slowed things down.
I decided to make some quick performance tests and here are the results:
- GLM’s matrix multiplications are 4 times slower compared to my custom one liner and using CUDA’s native vector types.
Dot and cross products of vectors are roughly 30% faster than the implementation found in the cuda samples (helper_math.h)
I used a GForce GTX 550 Ti, CUDA 6.5 and GLM 0.9.5.4 on linux for the test.
Hopefully the people behind GLM can fix it quickly as it is really a neat library. I submitted a bug report.
In case you want to see or even edit the source code of the tests, it lies on bitbucket, feel free to commit any changes : )
We were able to fix the performance problem in glm’s bug tracker. After aligning glm::vec4 properly, the matrix multiplication is almost equal and other functions (dot and cross) got even faster.
time for cuda glm (matrix): 233 milliseconds
time for cuda helper math (matrix): 226 milliseconds
time for cuda glm (dot): 185 milliseconds
time for cuda helper math (dot): 302 milliseconds
time for cuda glm (cross): 46 milliseconds
time for cuda helper math (cross): 162 milliseconds
The matrix multiplication indeed improved performance, but in the real world (well, my code :P ) this was only part of the issue. Additionally the tests I made triggered some kind of optimisation that was only possible in glm’s code. I discovered it when seeing that the results of those testing computations were either infinity or 0. I changed the values of the matrix/vectors so that the results stay in a range of [10*e-2, 1000] and vualà, the performance is almost the same.
Because of GLM running slower in my code, I conducted some additional tests and attempted to improve GLM’s performance. The performance didn’t improve enough, so I finally submitted another bug report.
The bug report was closed without any fixes to the lower performance. Additional aligned data types where added (aligned_vec4 etc), but the default ones won’t be aligned on CUDA.
## test results of my fastest glm version
#test 1 (synthetic)
time for cuda glm (matrix):.........546 milliseconds
time for cuda helper math (matrix):.660 milliseconds
time for cuda glm (dot):............471 milliseconds
time for cuda helper math (dot):....491 milliseconds
time for cuda glm (cross):..........246 milliseconds
time for cuda helper math (cross):..246 milliseconds
#test 2a (real life)
time for glm:.......................468 milliseconds
time for cuda:......................417 milliseconds
#test 2b (2a, but removed early exit from a loop)
time for glm:.......................373 milliseconds
time for cuda:......................370 milliseconds