Comments (10)
Just a quick comment about that: we actually have a preference in Brian2 exactly for this (core.default_float_dtype
), but this has been broken for quite a while now because we did not consequently use this preference and instead just assumed double
in quite a few places. Therefore, this preference will currently raise an error when you try to set it to single (see brian-team/brian2#417).
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Currently we are using double type everywhere except for random number generation with curand, where we generate floats... Any reason why this was implemented this way? If not, we should probably generate doubles by default?
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As long as brian-team/brian2#417 is not fixed, we could use our own preference and define our own c_data_type
function and transform numpy.float64
and numpy.float32
either both to float
or both to double
, depending on the preference.
Additionally the SynapticPathway
declaration on objects.cu
should use the same preference (currently hard coded to double
).
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We should use the brian-team/brian2@float32_support branch for our final benchmarks (potentially need to rebase on brian2 master), implement single precision from brian2CUDA side such that it works if the brian2 preference works and add a warning/error until brian2 has merged the float32_support
branch.
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ensure that our templates always use type from user preference (including random number generator, sizeof() etc.)
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find a reasonable solution to get float32 branch from brian2 that allows syncing to the brian2 master updates easily
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lets discuss the failing tests situation when everything is implemented and (seems to be) working
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compare also visually with examples cpp vs cuda
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Just saw this comment on the timestep
function implementation. Potential rounding error problems for single precision floats. We should think about that.
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seems like in brian-team/brian2#981 Marcel described by the remark that timestep and related quantities always should be doubles even in single precision mode, this seems very reasonable and to be followed
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Related Issues (20)
- Call reset kernel only with as many threads as there are spiking neurons (not as there are neurons in total)
- Refactor benchmarking scripts and update generated plots
- Check if storing the size of synapse groups is necessary? HOT 1
- Needs patch to run with Brian 2.4.2 HOT 2
- Optimize `StateMonitor`
- Impelement brian2cuda preference file support
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- Consider partitioning eventspaces when using `Subgroup`s HOT 4
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- Optimize our `SpikeMonitor` for `Subgroups`
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- Fix `ReferencError` in spatialneuron tests HOT 3
- Fix memory leak when having multiple `run` calls
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- Recent Brian2 update PR broke benchmark scripts HOT 1
- Brian2Cuda Uninstalls Brian2 2.5.1 and Installs 2.4.2 Which Won't Import HOT 3
- Brian2Hears and Brian2CUDA HOT 3
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