dklend122

11 ms·

61.
by dklend122 6y ago
That's patent false. Julia has abstractions that make it very easy to write generic code . Sure, no arbitrary loops though,..but a constrained subset will work (see kernel abstractions.jl)
62.
by dklend122 6y ago
You are very misinformed here. Julia doesn't have static typing and there are a very large set of problems that are trivially solved with it's type system. See the tables.jl ecosystem for example
63.
by dklend122 6y ago
It used to have slower dicts and string handling like around version 0.5. It's come a very very long way since then. Now at 1.5. It's much faster for arbitrary code
64.
by dklend122 6y ago
More like 9 to 10 devs
65.
by dklend122 6y ago
They are still paying a bunch of expensive devs to work on it, so maybe not.
66.
by dklend122 6y ago
Well, it's not happening in swift yet. S4TF still requires either c cuda kernels or XLA. Julia on the other hand has JIT GPU codegen and its CPU codegen has been benchmarked to beat openblas
67.
by dklend122 6y ago
Nope. PyCuda is writing inline C cuda and hardly integrated with python. Julia has four layers of GPU abstractions, each being successively easier to use ( bit a bit less control. ), all in pure Julia and reusing the compiler and normal Ju
68.
by dklend122 6y ago
And It's improved by miles for those things
69.
by dklend122 6y ago
Here's a Tulio example. Tulip also provides source to source derivatives. Both KA and Tulio work in the CPU with the same code using Tullio, OffsetArrays # A convolution with cyclic indices mat = zeros(10,10,1); mat[2,2] = 101; mat[10,
70.
by dklend122 6y ago
It answers your question because it's a specific case of KA's compile structured loops with annotations to low level CUDA code. If you could express the problem in generalized index notation, tullio.jl is an even higher level ab
71.
by dklend122 6y ago
That's a function of the available tools, not of the opportunities to do so. Julia makes writing custom kernels easy.
72.
by dklend122 6y ago
How is it not generic enough for the web? See https://genieframework.com/ and interact.jl Once it can compile it web assembly (work in progress) it be the obvious choice.
73.
by dklend122 6y ago
Julia libraries are plenty mature enough to be useful and Julia's 10x advantage in numerical computing far outweighs the difference in many cases.
74.
by dklend122 6y ago
As a library user, I far prefer Julia to python. With Julia I don't have to deal with python's ugly math syntax, terrible abstractions, SLOWWW custom types ) and 3-5 different array/collection "type" systems.
75.
by dklend122 6y ago
Jeremy has amended his comments regarding the viability of Julia Computing based on new information he received: https://twitter.com/jeremyphoward/status/1302678869158182912... The financials are more secure than
76.
by dklend122 6y ago
You can precompile a binary that bakes in the compiler and distribute that. Slimmer separate compilation is on the roadmap
77.
Julia Creators Reddit AMA(reddit.com)
2 points by dklend122 6y ago|0 comments
78.
Building Microservices and Applications in Julia(youtu.be)
1 points by dklend122 6y ago|0 comments
79.
by dklend122 6y ago
It's in the earlier comment-jaynes.jl
80.
by dklend122 6y ago
How are anonymous UUIDs PII and how is JC exploiting them? They have no special access nor do I see a first order effect that benefits them. Just that the open source Julia ecosystem will benefit and that will feed back into JC's mark
81.
Building microservices and applications in Julia Juliacon 2020(pretalx.com)
1 points by dklend122 6y ago|0 comments
82.
JuliaCon 2020 Talks(pretalx.com)
1 points by dklend122 6y ago|0 comments
83.
by dklend122 6y ago
In addition to the excellent answer by the author below, I'd like to say that Julia can get within spitting distance (or even sometimes exceed) C++ speeds (and even BLAS). So if a comparable amount of work went into optimizing speci
84.
by dklend122 6y ago
Would you mind opening corresponding issues on the repo? That would help guide the ongoing compiler work.
85.
Using type domain information in Julia(ericphanson.com)
2 points by dklend122 6y ago|0 comments
86.
by dklend122 6y ago
Turns out the allocations are just from the fact that the computation as written was not type stable. Making it so brings down the allocations to 2 and increases the relative speed to just under 2x. Here's the post from chris: "Th
87.
by dklend122 6y ago
The allocations for the StaticArrays solve must come from somewhere
88.
by dklend122 6y ago
I'd be interested to see a static arrays benchmark run on 1.5 as it can now stack allocate views and other types due to general compiler improvements.
89.
by dklend122 6y ago
You can use the @. macro to dot an entire block. I personally like the dot syntax. It's ergonomic, and provides cool stuff like this: https://julialang.org/blog/2017/01/moredots/ I don't know o
90.
by dklend122 6y ago
The below package implements something like that, but better imo: https://tk3369.github.io/BinaryTraits.jl/dev/

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