Comments (2)
Hey!
Thanks for the feedback! :)
It's nice to see people using and comparing it!
You are right that our implementation is comparatively a bit slow at the moment. Vectorization proved non-trivial in some cases, especially over the dimension. We will need to look into it more.
There are also some parameters, which require more tuning like # of vegas iterations and # of stratifications.
Can you provide some details on the settings you used? Then I will include them in the profiling that we will be doing to identify the bottlenecks. Of particular interest would be N, integrand and dimensionality.
One part that we were particularly struggling with is the weight accumulation in the vegas map here:
torchquad/torchquad/integration/vegas_map.py
Line 106 in 4e25083
If you have any thoughts on vectorizing that function we will gladly try them!
from torchquad.
Fixed in #137 , will be in the next release
from torchquad.
Related Issues (20)
- Change behavior of 'backend' HOT 6
- Elementwise numerical integration HOT 28
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- A lot of warnings in the current test CI on develop HOT 1
- Add tests for JIT HOT 2
- Evaluate many different integrands over the different domain HOT 6
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- Release 0.4.0 HOT 8
- Tests failing on GPU HOT 15
- torch.tensor containing integers as integration domain returns zero with non-compiled integrator HOT 2
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- Example/documentation for parametric domain of integration? HOT 9
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from torchquad.