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Firstly, thanks for PyO3, it's great!
This issue is being opened following a [chat post on gitter].(https://matrix.to/#/!AAhjIWoaKSExrkkhlG:gitter.im/$9gOL75NMgSzKVrKugcQUMBUFcy5QMpLvxuJafAHLieU?v…
Thell updated
9 months ago
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I recently wanted to see the assembly of `rand(UInt)`, and found this result somewhat disappointing:
```
julia> @code_native rand(UInt)
```
```asm
.text
.file "rand"
…
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A MWE:
```julia
julia> using Random, ForwardDiff
julia> N = 8;
julia> x = Vector{ForwardDiff.Dual{Nothing,Float64,1}}(undef, N);
julia> randn!(Xoshiro(1), x);
julia> x[end].value
-0.8…
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We have to keep MersenneTwister around and thus keep dSFMT as a dependency of Julia itself even though we no longer use it as our default PRNG. For 2.0 we should move it out and consider exporting no …
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I just noticed that the current default Xoshiro RNG generates uniform floats with more entropy than other RNGs. The reason appears to be that it has it's own 24/53-bit implementation of the conversion…
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```
julia> rng = Xoshiro(1776)
julia> x
10-element Vector{Float64}:
0.4552384158732863
0.5476424498276177
0.7733535276924052
0.9405848223512736
0.02964765308691042
0.74694291453392
…
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Unsure how close to working this is supposed to be, but I spotted a few things whilst trying to understand the parallel random implementation.
https://github.com/johnlees/dust-gpu-prototype/blob/ma…
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I have updated MLJTestInterface.jl to test models on row-based tables and discovered this bug:
```julia
julia> using SIRUS, MLJTestInterface, MLJBase
julia> X, y = MLJTestInterface.make_regress…
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Hi I'm trying to build the solution on Ubuntu linux but I receive this error
src/trlwe_compressed_vaes.c:16:17: warning: AVX512F vector return without AVX512F enabled changes the ABI [-Wpsabi]
1…
Claoo updated
6 months ago
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Right now the support for OpenMP has a lot of overhead, to the extent that it is counterproductive to enable it when we precompute probabilities. This is potentially due to
- the xoshiro RNG whic…