wallnuss

10 ms·

31.
by wallnuss 9y ago
I still don't quite see why you wouldn't just use a named struct, but you can create a alias for the NamedTuple type const MyAlias = NamedTuple{(:a, :b),Tuple{Int64,Int64}} MyAlias((2, 3)) # (a = 2, b = 3)
32.
by wallnuss 9y ago
Threads.@threads is still consider a bit experimental and there are a few performance pitfalls that one can stumble into. I talked about this for a bit in http://slides.com/valentinchuravy/julia-parallelism , but if you
33.
by wallnuss 9y ago
Important to note that the 0.7 release is not quite done yet and we are polishing it. So there is no official release candidate yet.
34.
by wallnuss 9y ago
My favourite sentence is: Note: I've never actually tried this, so we'll just assume it works.
35.
AI-HPC is Happening Now(insidehpc.com)
3 points by wallnuss 9y ago|1 comments
36.
by wallnuss 9y ago
The blog is fun introduction into JIT compiler, but the big thing that is missing and that makes Python a hard problem for JIT is supporting Objects. From http://blog.kevmod.com/2017/02/personal-thoughts-about-pyst
37.
by wallnuss 9y ago
Yeah I know and the DWARF info special cases are even worse for ptxas. I never had enough time, but Nvidia has surprisingly a lot information on it out there.
38.
by wallnuss 9y ago
The big difference is that Julia can handle user defined structs and handle higher-level functions, e.g. you pass a Julia function to you GPU kernel and that function will get compile for the GPU without you having to declare it GPU-compati
39.
by wallnuss 9y ago
ROCm will make similar things possible! I prefer native codegen, CLArrays.jl is an excellent solution in the meantime.
40.
by wallnuss 9y ago
I spend a while looking at debug information for NVPTX last year and came to the conclusion that it luckily dwarf, with some weird serialisation for the assembler. The NVPTX backend would benefit imo to move towards the more general LLVM in
41.
by wallnuss 9y ago
You are absolutely right, an optimised C Programn would be as fast or faster than the Numpy implementation.
42.
by wallnuss 9y ago
If you have a particular algorithm that is better expressed using 0-based indexing use https://github.com/JuliaArrays/OffsetArrays.jl
43.
by wallnuss 9y ago
In my opinion that is the wrong question to ask. The right question is: How fast will my code be. Numpy has been heavily optimised and is written in C and not Python. Take a look at the link below, comparing a simple sum in different langua
44.
by wallnuss 9y ago
* My entire codebase Julia has great interoperability between Python (checkout PyCall.jl) and many other languages. You don't need to abandon your old codebase. With regards to your other points, a lot of that is subjective and hard to
45.
by wallnuss 9y ago
how??! In what context does a clear-text password end up anywhere, except as the input for a hash function.
46.
by wallnuss 9y ago
I am slowly preparing to depart my lovely xmonad setup for gnome due to issues with HiDPI..., but I still love the multi-screen support in xmonad.
47.
by wallnuss 9y ago
Given that CUDAnative.jl is beating CUDA c in some benchmarks and in others it is a bit slower, I would suspect that NUMBA is similar in performance. The thing where the Julia CUDA support really shines is that it is supporting arbitrary Ju
48.
by wallnuss 9y ago
You are right that the current adoption rate of Python makes it seem like Julia is in a losing position, but on the other hand it took Python a long time to get in to the position it is right now and systemic changes take time. The two argu
49.
by wallnuss 9y ago
The only sad part of this story is the design was meant to celebrate John Conway and his Game of Life [1] (Conway actually was a lecturer at Cambridge, when he introduced GoL). [1] From 2014: http://www.railwaygazette.com/ne
50.
JuliaCon 2017: List of talks(juliacon.org)
3 points by wallnuss 9y ago|0 comments
51.
JuliaCon 2016: Call for Proposals(juliacon.org)
1 points by wallnuss 10y ago|0 comments
52.
by wallnuss 10y ago
I see that there is a llvm backend at https://github.com/adapteva/epiphany-llvm , but it hasn't been updated in a while. Are there any plans on upstreaming/contributing and maintaining a backend for llvm?
53.
Responsive Visualizations in Julia(randomfantasies.com)
2 points by wallnuss 10y ago|0 comments
54.
Julia API for Tensorflow(github.com)
2 points by wallnuss 10y ago|0 comments
55.
MXNet: DMLC for Scalable and Reliable Machine Learning(dmlc.ml)
1 points by wallnuss 11y ago|0 comments
56.
by wallnuss 12y ago
You cannot do Pkg.update("MyPackage", but you are able to do Pkg.build("MyPackage") and Pkg.test("MyPackage"). Pkg.update() is currently designed to update the local METADATA snapshot and then update all packag