Seyoon Ko

@lostella Thank you for the listing. I was thinking about separate implementations for GPU because with multi-threading support (#82) in consideration, efficient implementations for CPU and GPU would not coincide....

I have the same issue on OSX with OpenCL.jl v0.8.0. ``` julia> versioninfo() Julia Version 1.1.1 Commit 55e36cc (2019-05-16 04:10 UTC) Platform Info: OS: macOS (x86_64-apple-darwin15.6.0) CPU: Intel(R) Core(TM) i7-7820HQ...

Oh no. It's blocked by `Transpiler.jl` not supporting 1.0. https://github.com/SimonDanisch/Transpiler.jl/issues/28 :cry:

I'm not sure about the point of this PR. Could you explain more about it? The original code imputes the missing value with the column mean, and the proposed code...

How about running precompilation with a smaller computation task? This might or might not help I guess...

Could we try to resolve it by just qualifying the maf() call as suggested in the warning message?

I might be able to help with the Cox model. I'm currently working on a proximal gradient algorithm w/L1 penalization with it.

@olivierlabayle Yes, we can definitely consider it.

That is because of updates in BenchmarkTools.jl I believe. Maybe you should look for plain text-based outputs from it?

Thanks for the report. How many threads were used? I wrote matrix-vector multiplication three years ago, and there could be room for improvement. I will look into the `GX` part....