Comments (4)
I'd say it is pretty hard - the algorithms are very sequential by nature, which is not enjoyed by GPUs. The most promising approach might be to parallelise across events, rather than try to parallelise within an event.
Alternatively, one could investigate more parallelisable clustering approaches, like cellular automata or maybe even an ML inference algorithm for clustering.
Stretch goal, but interesting...
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Sort of... but one would need to see if the code for the CPU version is going to go onto a GPU to do each of the events. The data structures for the plain algorithm would be a lot easier and would probably accelerate well. For the tiled algorithm my gut feeling is it would be a lot of work, as the whole thrust there is to use more complex logic to reduce the computational burden. e.g., linked lists and the whole tiling setup, so the data layouts are not at all GPU friendly.
So the plain algorithm would be the one to target IMO.
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I was thinking parallel over events! Which should boils down to writing some loops with KernalAbstrations.jl maybe?
from jetreconstruction.jl.
Although I should say from the outset that at the typical particle densities where N2Plain is used (Z->ee) the typical jet reconstruction time is O(10Ī¼s), so this is not, per-se, a real target for GPU running.
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Related Issues (20)
- Improve tests HOT 1
- Nicer strategy switching
- Defaut strategy
- Return consistent sequence types
- Remove internal data from PseudoJet HOT 1
- Return type should be a ClusterSequence
- Remove tiling parameter from current ClusterSequence
- Write proper documentation
- Implement exclusive jet methods
- Simpler examples
- Plotting support HOT 3
- Invoke algorithm by enum, not by power value HOT 3
- Add generalised kt algorithm for e+e- HOT 1
- Add kt algorithm for e+eā
- More regular code formatting HOT 1
- Use GLMakie for on-screen example plots
- TagBot trigger issue HOT 3
- Examples are broken
- Support retrieval of jet constituents HOT 2
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