Comments (8)
Haha, yeah, those hard-coded UInt64 just might be the culprit. ;)
Unsurprisingly, this did the trick. Thanks a lot for the quick fix!
from vectorizationbase.jl.
Hopefully fixed by 67d5236
from vectorizationbase.jl.
The tests segfault on x86 Windows.
If that's a problem, feel free to file another issue.
I don't have a Windows machine on which to test and debug.
Tests pass on Linux.
from vectorizationbase.jl.
Unfortunately I still receive the exact same error message with v0.21.19 on both x86 linux and Windows.
I only use github actions to test my package on x86 builds too, this is where this shows up. I don't have any x86 os locally to further test this.
Here are the probably most relevant parts of the stacktrace (still long!), maybe its is just showing in the context where it is called.
Got exception outside of a @test
LoadError: MethodError: no method matching vload_transpose_quote(::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Bool, ::Int32, ::Int32, ::UInt64, ::Bool)
Closest candidates are:
vload_transpose_quote(::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Int32, ::Bool, ::Int32, ::Int32, ::UInt32, ::Bool) at /home/runner/.julia/packages/VectorizationBase/i9XbD/src/vecunroll/memory.jl:204
Stacktrace:
[1] #s33#326
@ ~/.julia/packages/VectorizationBase/i9XbD/src/vecunroll/memory.jl:413 [inlined]
[2] var"#s33#326"(A::Any, AU::Any, F::Any, N::Any, AV::Any, W::Any, M::Any, I::Any, T::Any, D::Any, RS::Any, UX::Any, C::Any, ::Any, sptr::Any, u::Any, sm::Any, #unused#::Type, #unused#::Type, #unused#::Any)
@ VectorizationBase ./none:0
[3] (::Core.GeneratedFunctionStub)(::Any, ::Vararg{Any, N} where N)
@ Core ./boot.jl:571
[4] _vload
@ ~/.julia/packages/VectorizationBase/i9XbD/src/vecunroll/memory.jl:440 [inlined]
[5] vload
@ ~/.julia/packages/VectorizationBase/i9XbD/src/llvm_intrin/memory_addr.jl:681 [inlined]
[6] macro expansion
@ ~/.julia/packages/TriangularSolve/90Qqf/src/TriangularSolve.jl:229 [inlined]
[7] rdiv_solve_W!
@ ~/.julia/packages/TriangularSolve/90Qqf/src/TriangularSolve.jl:220 [inlined]
[8] rdiv_U!(spc::LayoutPointers.StridedPointer{Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{0}, Static.StaticInt{0}}}, spa::LayoutPointers.StridedPointer{Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{0}, Static.StaticInt{0}}}, spu::LayoutPointers.StridedPointer{Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{0}, Static.StaticInt{0}}}, M::Int32, N::Int32, #unused#::Static.StaticInt{2}, #unused#::Val{true})
@ TriangularSolve ~/.julia/packages/TriangularSolve/90Qqf/src/TriangularSolve.jl:515
[9] div_dispatch!(C::StrideArraysCore.PtrArray{Tuple{Int32, Int32}, (false, true), Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}}, A::StrideArraysCore.PtrArray{Tuple{Int32, Int32}, (false, true), Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}}, U::StrideArraysCore.PtrArray{Tuple{Int32, Int32}, (false, true), Float64, 2, 2, 0, (2, 1), Tuple{Int32, Static.StaticInt{8}}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}}, #unused#::Val{true}, #unused#::Val{true})
@ TriangularSolve ~/.julia/packages/TriangularSolve/90Qqf/src/TriangularSolve.jl:314
[10] ldiv! (repeats 2 times)
@ ~/.julia/packages/TriangularSolve/90Qqf/src/TriangularSolve.jl:343 [inlined]
[11] reckernel!(A::StrideArraysCore.PtrArray{Tuple{Int32, Int32}, (true, true), Float64, 2, 1, 0, (1, 2), Tuple{Static.StaticInt{8}, Int32}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}}, pivot::Val{true}, m::Int32, n::Int32, ipiv::StrideArraysCore.PtrArray{Tuple{Int32}, (true,), Int32, 1, 1, 0, (1,), Tuple{Static.StaticInt{4}}, Tuple{Static.StaticInt{1}}}, info::Int32, blocksize::Int32, thread::Bool)
@ RecursiveFactorization ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:165
[12] reckernel!(A::StrideArraysCore.PtrArray{Tuple{Int32, Int32}, (true, true), Float64, 2, 1, 0, (1, 2), Tuple{Static.StaticInt{8}, Int32}, Tuple{Static.StaticInt{1}, Static.StaticInt{1}}}, pivot::Val{true}, m::Int32, n::Int32, ipiv::StrideArraysCore.PtrArray{Tuple{Int32}, (true,), Int32, 1, 1, 0, (1,), Tuple{Static.StaticInt{4}}, Tuple{Static.StaticInt{1}}}, info::Int32, blocksize::Int32, thread::Bool)
@ RecursiveFactorization ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:159
[13] recurse!
@ ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:76 [inlined]
[14] lu!(A::Matrix{Float64}, ipiv::Vector{Int32}, pivot::Val{true}; check::Bool, blocksize::Int32, threshold::Int32)
@ RecursiveFactorization ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:62
[15] #lu!#2
@ ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:27 [inlined]
[16] lu! (repeats 2 times)
@ ~/.julia/packages/RecursiveFactorization/7pQ6n/src/lu.jl:22 [inlined]
[17] (::DiffEqBase.DefaultLinSolve)(x::Vector{Float64}, A::Matrix{Float64}, b::Vector{Float64}, update_matrix::Bool; reltol::Float64, kwargs::Base.Iterators.Pairs{Symbol, DiffEqBase.ScaleVector{Vector{Float64}}, Tuple{Symbol, Symbol}, NamedTuple{(:Pl, :Pr), Tuple{DiffEqBase.ScaleVector{Vector{Float64}}, DiffEqBase.ScaleVector{Vector{Float64}}}}})
@ DiffEqBase ~/.julia/packages/DiffEqBase/YIT73/src/linear_nonlinear.jl:112
[18] perform_step!(integrator::OrdinaryDiffEq.ODEIntegrator{OrdinaryDiffEq.Rosenbrock23{11, false, DiffEqBase.DefaultLinSolve, Val{:forward}}, true, Vector{Float64}, Nothing, Float64, Vector{Any}, Float64, Float64, Float64, Float64, Vector{Vector{Float64}}, SciMLBase.ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Any}, SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.Rosenbrock23{11, false, DiffEqBase.DefaultLinSolve, Val{:forward}}, OrdinaryDiffEq.InterpolationData{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Rosenbrock23Cache{Vector{Float64}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, OrdinaryDiffEq.Rosenbrock23Tableau{Float64}, SciMLBase.TimeGradientWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Float64}, Vector{Any}}, SciMLBase.UJacobianWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Float64, Vector{Any}}, DiffEqBase.DefaultLinSolve, FiniteDiff.JacobianCache{Vector{Float64}, Vector{Float64}, Vector{Float64}, UnitRange{Int32}, Nothing, Val{:forward}(), Float64}, FiniteDiff.GradientCache{Nothing, Vector{Float64}, Vector{Float64}, Float64, Val{:forward}(), Float64, Val{true}()}}}, DiffEqBase.DEStats}, SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, OrdinaryDiffEq.Rosenbrock23Cache{Vector{Float64}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, OrdinaryDiffEq.Rosenbrock23Tableau{Float64}, SciMLBase.TimeGradientWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Float64}, Vector{Any}}, SciMLBase.UJacobianWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Float64, Vector{Any}}, DiffEqBase.DefaultLinSolve, FiniteDiff.JacobianCache{Vector{Float64}, Vector{Float64}, Vector{Float64}, UnitRange{Int32}, Nothing, Val{:forward}(), Float64}, FiniteDiff.GradientCache{Nothing, Vector{Float64}, Vector{Float64}, Float64, Val{:forward}(), Float64, Val{true}()}}, OrdinaryDiffEq.DEOptions{Float64, Float64, Float64, Float64, OrdinaryDiffEq.PIController{Rational{Int32}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(LinearAlgebra.opnorm), Nothing, DiffEqBase.CallbackSet{Tuple{}, Tuple{DiffEqBase.DiscreteCallback{SDtoolbox.CV.var"#approaches_equilibrium#2"{Float64}, typeof(SciMLBase.terminate!), typeof(DiffEqBase.INITIALIZE_DEFAULT), typeof(DiffEqBase.FINALIZE_DEFAULT)}}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int32, Tuple{}, Tuple{}, Tuple{}}, Vector{Float64}, Float64, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Rosenbrock23Cache{Vector{Float64}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, OrdinaryDiffEq.Rosenbrock23Tableau{Float64}, SciMLBase.TimeGradientWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Float64}, Vector{Any}}, SciMLBase.UJacobianWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Float64, Vector{Any}}, DiffEqBase.DefaultLinSolve, FiniteDiff.JacobianCache{Vector{Float64}, Vector{Float64}, Vector{Float64}, UnitRange{Int32}, Nothing, Val{:forward}(), Float64}, FiniteDiff.GradientCache{Nothing, Vector{Float64}, Vector{Float64}, Float64, Val{:forward}(), Float64, Val{true}()}}, repeat_step::Bool)
@ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/t7j8H/src/perform_step/rosenbrock_perform_step.jl:113
[19] perform_step!
@ ~/.julia/packages/OrdinaryDiffEq/t7j8H/src/perform_step/rosenbrock_perform_step.jl:95 [inlined]
[20] solve!(integrator::OrdinaryDiffEq.ODEIntegrator{OrdinaryDiffEq.Rosenbrock23{11, false, DiffEqBase.DefaultLinSolve, Val{:forward}}, true, Vector{Float64}, Nothing, Float64, Vector{Any}, Float64, Float64, Float64, Float64, Vector{Vector{Float64}}, SciMLBase.ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Any}, SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.Rosenbrock23{11, false, DiffEqBase.DefaultLinSolve, Val{:forward}}, OrdinaryDiffEq.InterpolationData{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Rosenbrock23Cache{Vector{Float64}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, OrdinaryDiffEq.Rosenbrock23Tableau{Float64}, SciMLBase.TimeGradientWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Float64}, Vector{Any}}, SciMLBase.UJacobianWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Float64, Vector{Any}}, DiffEqBase.DefaultLinSolve, FiniteDiff.JacobianCache{Vector{Float64}, Vector{Float64}, Vector{Float64}, UnitRange{Int32}, Nothing, Val{:forward}(), Float64}, FiniteDiff.GradientCache{Nothing, Vector{Float64}, Vector{Float64}, Float64, Val{:forward}(), Float64, Val{true}()}}}, DiffEqBase.DEStats}, SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, OrdinaryDiffEq.Rosenbrock23Cache{Vector{Float64}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, OrdinaryDiffEq.Rosenbrock23Tableau{Float64}, SciMLBase.TimeGradientWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Float64}, Vector{Any}}, SciMLBase.UJacobianWrapper{SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Float64, Vector{Any}}, DiffEqBase.DefaultLinSolve, FiniteDiff.JacobianCache{Vector{Float64}, Vector{Float64}, Vector{Float64}, UnitRange{Int32}, Nothing, Val{:forward}(), Float64}, FiniteDiff.GradientCache{Nothing, Vector{Float64}, Vector{Float64}, Float64, Val{:forward}(), Float64, Val{true}()}}, OrdinaryDiffEq.DEOptions{Float64, Float64, Float64, Float64, OrdinaryDiffEq.PIController{Rational{Int32}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(LinearAlgebra.opnorm), Nothing, DiffEqBase.CallbackSet{Tuple{}, Tuple{DiffEqBase.DiscreteCallback{SDtoolbox.CV.var"#approaches_equilibrium#2"{Float64}, typeof(SciMLBase.terminate!), typeof(DiffEqBase.INITIALIZE_DEFAULT), typeof(DiffEqBase.FINALIZE_DEFAULT)}}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int32, Tuple{}, Tuple{}, Tuple{}}, Vector{Float64}, Float64, Nothing, OrdinaryDiffEq.DefaultInit})
@ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/t7j8H/src/solve.jl:478
[21] #__solve#493
@ ~/.julia/packages/OrdinaryDiffEq/t7j8H/src/solve.jl:5 [inlined]
[22] #solve_call#42
@ ~/.julia/packages/DiffEqBase/YIT73/src/solve.jl:61 [inlined]
[23] solve_up(prob::SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Any}, SciMLBase.ODEFunction{true, typeof(SDtoolbox.CV.cv!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, sensealg::Nothing, u0::Vector{Float64}, p::Vector{Any}, args::OrdinaryDiffEq.Rosenbrock23{0, false, DiffEqBase.DefaultLinSolve, Val{:forward}}; kwargs::Base.Iterators.Pairs{Symbol, Any, NTuple{4, Symbol}, NamedTuple{(:progress, :callback, :abtol, :reltol), Tuple{Bool, DiffEqBase.DiscreteCallback{SDtoolbox.CV.var"#approaches_equilibrium#2"{Float64}, typeof(SciMLBase.terminate!), typeof(DiffEqBase.INITIALIZE_DEFAULT), typeof(DiffEqBase.FINALIZE_DEFAULT)}, Float64, Float64}}})
@ DiffEqBase ~/.julia/packages/DiffEqBase/YIT73/src/solve.jl:87
[24] #solve#43
@ ~/.julia/packages/DiffEqBase/YIT73/src/solve.jl:73 [inlined]
from vectorizationbase.jl.
Unfortunately I still receive the exact same error message with v0.21.19 on both x86 linux and Windows.
I only use github actions to test my package on x86 builds too, this is where this shows up. I don't have any x86 os locally to further test this.
Please provide a reproducer, or at least a stack trace.
The only information I have to go off of here is that the indicated function received a UInt64
argument when it should have received UInt32
.
But received from what caller, where?
from vectorizationbase.jl.
I don't have any x86 os locally to further test this.
If you have an x64 OS locally, you should be able to just install and run an x86 binary on the x64 system.
https://julialang.org/downloads/
You may additionally have to install 32-bit support libraries if you don't have them.
from vectorizationbase.jl.
Unfortunately I still receive the exact same error message with v0.21.19 on both x86 linux and Windows.
I only use github actions to test my package on x86 builds too, this is where this shows up. I don't have any x86 os locally to further test this.Please provide a reproducer, or at least a stack trace. The only information I have to go off of here is that the indicated function received a
UInt64
argument when it should have receivedUInt32
. But received from what caller, where?
Sorry, for the missing information! I added a stacktrace.
And thanks for the hint regarding 32bit Julia on a 64bit system, I will try this as soon as possible.
from vectorizationbase.jl.
Thanks for the stacktrace.
Should be fixed by: JuliaSIMD/TriangularSolve.jl@e5d987c
from vectorizationbase.jl.
Related Issues (20)
- Precompilation breaks for non-native target HOT 1
- Segfault in `__vload` on 1.11 HOT 1
- Define `VectorizationBase.CACHE_COUNT`, etc. in the module `__init___()` function HOT 9
- Definition of `const CACHE_LEVELS` causing precompilation to fail on Manjaro Linux HOT 3
- StackOverflowError on VectorizationBase v0.15 HOT 2
- Feature request: how many sockets does my machine have? HOT 8
- Problem statement/MWE of the relocatability issue HOT 5
- World age errors when using VectorizationBase with `--compiled-modules=no`
- Contiguous not defined error when Precompiling HOT 7
- StackOverflowError: vsub(a::UInt128, b::UInt128) HOT 1
- Win10, error with building VectorizationBase? HOT 11
- Commit "Fast integer ops shouldn't wrap" slowed down loading from PtrArrays with four or more dimensions HOT 3
- InitError when porting precompiled module HOT 1
- Cut VectorizationBase into pieces HOT 2
- UInt256 not defined HOT 1
- Docstring for VecUnroll? HOT 5
- "this intrinsic must be compiled to be called" HOT 4
- Precompiling VectorizationBase errors in Ubuntu, julia 1.7.2 HOT 5
- VectorizationBase.jl breaks StaticArrays.jl HOT 4
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from vectorizationbase.jl.