Chris Elrod

> And is it okay for using RCall in Julia 1.3? No https://github.com/JuliaInterop/RCall.jl/issues/326 I realized after my last comment that JuliaCall requires RCall, otherwise I would have tried to resolve...

Figured it is better for me to comment here than open another issue. I am also getting segfaults every time, as well as a ReadOnlyMemoryError. ```julia julia> versioninfo() Pkg.stJulia Version...

Okay, cool -- trying amdgpu-pro on the second computer, I'm able to build CLBLAS but ran into two issues: 1) https://github.com/JuliaGPU/OpenCL.jl/issues/123 2) The graphics no longer seemed to be working,...

Try building Julia from source with this in your `Make.user`: ``` override USE_LLVM_SHLIB = 0 ``` I had a lot of different types of Mesa-related segfaults that were all solved...

```julia julia> Pkg.checkout("CLArrays") INFO: Checking out CLArrays master... INFO: Pulling CLArrays latest master... INFO: Cloning cache of Adapt from https://github.com/MikeInnes/Adapt.jl.git INFO: Installing Adapt v0.2.0 julia> using CLArrays, GPUArrays INFO: Recompiling...

I'm on master of `OpenCL`, `Transpiler`, and `CLArrays`. The problem seems serious, and is persistent. ```julia julia> c1 .= 4f0;#works julia> c2 .= 2f0; julia> Array(c2) Memory access fault by...

> So on that note, it would be good to see a test of LoopVectorization against something like NNLib's conv for the nonlinear Laplacian. I have faith it could match...

I played with it a bit, and haven't been able to get any major improvements over the explicit loop version: ```julia using LinearAlgebra, OrdinaryDiffEq # Generate the constants p =...

Those loops are easy enough for LLVM to SIMD, and there's not too much that can be done to make them faster. I.e., there isn't much reuse you can get...

Making them dense: ```julia Ax = Array(Tridiagonal([1.0 for i in 1:N-1],[-2.0 for i in 1:N],[1.0 for i in 1:N-1])) ``` adds a huge amount of computation, making it go from...