ViralBShah

9 ms·

31.
by ViralBShah 6y ago
The way to do this is to find non-domain specific tasks in these projects, and make useful contributions to the team - and slowly learning as you go along. Website, CI, benchmarking, helping out new users, pointing out unclear docs, writing
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by ViralBShah 6y ago
Scientific computing has always been around, but as you allude, a bit in the background. It used to be matlab/mathematica on your desktop or a supercomputer that very few could get access to and program. With cloud computing, GPUs, abi
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The team shaking up climate models(csmonitor.com)
1 points by ViralBShah 6y ago|1 comments
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by ViralBShah 6y ago
Thanks for the explanation. I feel that Julia has always been well received on HN ever since we publicly announced it in 2012. I believe that post v1, there are just more users out there and more blogs are being written, more companies are
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by ViralBShah 6y ago
Julia Computing is about 40 people, and that is a recent thing.
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Tiobe Index for January 2021(tiobe.com)
2 points by ViralBShah 6y ago|1 comments
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by ViralBShah 6y ago
It has been reported that Julia runs just fine under Rosetta. https://github.com/JuliaLang/julia/issues/36617#issuecomment...
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by ViralBShah 6y ago
Yes, you can use PackageCompiler for your collection of packages. We are also exploring doing this in the background when you add more packages. The reason why we can't distribute precompiled .ji files at a package level is because of
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by ViralBShah 6y ago
That's a completely reasonable viewpoint. Many users of Julia and contributors start out experimenting with it and then end up bringing it into their work when they feel comfortable with it. I hope you will have the same experience one
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by ViralBShah 6y ago
Nobody is spinning anything here. Julia's compilation time is a known quantity. See my comment in https://news.ycombinator.com/item?id=24750559 about the different phases of Julia's compilation and execution (from
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by ViralBShah 6y ago
There are 3 different phases: 1. Precompilation time: Only once when you install or upgrade a package. 2. First running time (that includes compilation time): When you call a function like `plot` the first time in a session 3. Second and su
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by ViralBShah 6y ago
Yes, Gadfly does have a large compile time. Reducing the time to first plot (essentially compilation time) has been a major focus for the 1.5 release. People often use Plots.jl or PyPlot which have significantly lesser compile times.
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by ViralBShah 6y ago
Perhaps not directly relevant to your point here, but thought it would be interesting to anyone following along. Jacob Quinn (karbacca) also has a Julia package for integrating Julia into the Arrow ecosystem: https://github.com&#
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by ViralBShah 6y ago
I tried out the dataset you mention. I use the latest Julia 1.5.2 and CSV.jl 0.7.7 and load the CSV package in 1s, read the file first time in 5s and then in 0.03s the second time. The precompile time you mention is only for the very first
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by ViralBShah 6y ago
We've certainly tested all Float32 inputs to some functions in Julia's math packages. I'm unable to find the relevant issues this very moment, otherwise would certainly post them.
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by ViralBShah 6y ago
See Jacob Quinn's (karbarcca) other package: https://github.com/JuliaWeb/HTTP.jl It is quite common for scientists and engineers to want to present the findings of their work or share them with others who are non-
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by ViralBShah 6y ago
You can write slow programs in all languages. There are many cases where Julia's language features enable higher performance than in comparable C libraries. Have a look at Steve Johnson's keynote from JuliaCon 2019: https:/&
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by ViralBShah 6y ago
Wouldn't this statement be true for any new technology? I don't doubt what you say about being burnt when adopting in the early days. However, it is simultaneously true that people have successfully used Julia in several commercia
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by ViralBShah 6y ago
Static compilation works - see PackageCompiler.jl.
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by ViralBShah 6y ago
Yes, common lisp has certainly had multiple dispatch for decades. I think what makes Julia interesting is that the Dispatch Ratio and reuse is significantly higher in Julia than in other languages with multiple dispatch. Some of that is cer
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by ViralBShah 6y ago
I just realized that people who are not into scientific computing may not know what GEMM means. It is a matrix multiplication kernel. https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...
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Flexible Performant GEMM Kernels on GPUs in Native Julia(juliagpu.org)
6 points by ViralBShah 6y ago|1 comments
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by ViralBShah 6y ago
In general, in the Julia community, we are all used to see Keno do impressive stuff routinely, but this one tops everything so far. What is particularly impressive is that this lowest of the low-level debugging work is done completely in Ju
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GSoC 2020 Wrap-Up for Julia projects(julialang.org)
3 points by ViralBShah 6y ago|0 comments
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JuliaLang GSoC 2020 Wrap-Up(julialang.org)
14 points by ViralBShah 6y ago|0 comments
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by ViralBShah 6y ago
Not that I am aware of. We are working towards that possibility on JuliaHub.com, but nothing soon. I should point out that installing Julia and Pluto locally should take at best a matter of minutes and no fuss.
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by ViralBShah 6y ago
Note that the Pluto notebooks are not yet posted, but will be after the assignments are due.
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by ViralBShah 6y ago
One thinks that language does not matter - but it often does. While conceptually they are all the same, Julia, through its performance allows one to achieve high performance without needing to write kernels in C. When one is writing computa
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by ViralBShah 6y ago
The course is really for anyone who has a curiosity in applying computational science to solving problems. If you are motivated by Victor's essay on "What can a technologist do about Climate change" ( http://worrydr
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by ViralBShah 6y ago
I'll highly recommend "Ashoka: The Search for India's Lost Emperor". https://www.amazon.com/Ashoka-Search-Indias-Lost-Emperor/dp/... Not so much about Chanakya, but about that period in India,

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