It is earth science based. A lot of plotting, Machine Learning, Wave modeling. Finite elements, Signal processing. Mapping! A lot of physicists, mathematicians and computer scientist with applications to study the earth
What do you guys think about the potential of bringing people over to Julia? I mean I think i a great language to start (you can write live C or like Matlab), but I see a lot of seasoned coders here too!. Thoughts?
Hi Chris, you are an extremely generous man, but how could most of us possibly build things like Tullio, DiffEqs, LoopVectorization, Makie, and so many other high caliber packages that do make a difference? In the large base of the pyramid there are probably users with a little bit of DIY code thrown in. But I should add that a lot can be achieved by composing existing packages.
Building something to solve problems that someone cannot solve today can include using all of those tools. As a nice example of this, I like to point to Dan Padilha’s JuliaCon talk:
Using ModelingToolkit + DiffEq he got a domain-specific package that was 10x faster and has lots of cool new features for his domain. While MTK technically does this, until he came around “Julia could improve performance of designing spacecraft trajectories” was high level, theoretical, and speculative. Now there’s a package and video, go use it. There are tons of domains that need things pieced together correctly. If you package all of that up in a well-documented Julia package that clearly describes its advantages, then I have found that people will be happy to adopt it. So you don’t need to necessarily build the next LoopVectorization.jl, but if you can bring it to your domain, people will be ecstatic and that will be a major way to get the people around you using it.
On the earth science mapping front, geospatial tools are in heavy development currently. A lot of things will still seem harder than R/Python/Matlab and definitely less documented. But that is rapidly changing.
The interop is getting very good - increasingly geospatial packages work together seamlessly without converting objects manually (via GeoInterface.jl). We are working on integrating Makie.jl everywhere, which will be really amazing for interactive spatial visualizations.
In Rasters.jl I’m implementing super fast rasterization/coverage/masking/zonal stats (like 1-2 orders of magnitude faster than GDAL). It seems like we can do this for a lot of spatial operations in the medium term, algorithms are just so much easier to optimize and make parallel in Julia than in C++/R etc.
We partly have XArray-like features already with DiskArrays.jl, but they should get really good in the next year.
But: a lot of this is not easily demonstrable to a team at this point. A lot of it works, but with caveats.
So as @ChrisRackauckas says, it is better if you just use the language in your work for specific projects, and demonstrate the things that are 10/100 x faster and/or much cleaner to write, as you find them. Use julia to help with problems you cant solve in other languages. And help us meet your needs with issues, feature requests and PRs as you go.
You may be missing the central point a lot of us are trying to make - there is no easy generic comparison like this, and just “trying julia” is probably a waste of peoples time without a specific goal.
For some basic things Julia is far worse than other languages, because the tooling is missing or unfinished. For some things, especially high performance applications, Julia is often many times better than other languages. These areas are increasing, and the areas it is worse are shrinking. But thats a long process.
So the thing that matters most for your organization if there is currently an overlap between their specific needs and the things that Julia is much better at.
That is 99% of what will convince people at this stage of Julia uptake.
There you’ll see, in particular, that modern fortran syntax is quite simple and clean, by the way. So this is why there are other things in Julia that can interest fortran developers, such as how easy is to benchmark, package, document, distribute, and integrate the code with plotting and other tools, while retaining the performance (or perhaps improving it with the aid of the tooling by tuning the algorithms).
I really liked H2O.AI’s dataframes comparison, but they’ve stopped updating a year and a half ago (and lots of things have changed since then), and it’s quite expensive to run! Database-like ops benchmark
Well, if you want something simple (simple things always have a lot of power but also a potential bias of their own) to compare different languages, there is this exercise comparing 7 languages (Python, C++, Rust, Perl, R, Julia, FORTRAN) here.
I first come to this forum in 2022 and amazed by the enthusiasts and responds of almost all my questions are being answered.
You can read the wikipedia and the Notable Uses of Julia:
The Federal Reserve Bank of New York builds macroeconomic models in Julia in 2015 (ported from MATLAB), and for estimating COVID-19 shocks in 2021
BlackRock, the world's largest asset manager, for financial time series analysis
Aviva, the UK's largest general insurer, for actuarial calculations
Mitre Corporation, for verification of published election results
Nobel laureate Thomas J. Sargent, for macroeconometric modeling
I combine Julia with Python sometimes while learning Calculus for Undergraduate (till today), but Python code can be executed in Julia with PyCall…
Open source is the great advantage here, since MATLAB is expensive. C and Fortran has the highest speed but you need to code longer in order to gain the same output. We will still need C and Fortran as the bottom of food chain.
I installed MATLAB and never open it again after busy with Julia. Some output that I want are not yet available or can be achieved in Julia thus I use Python. For example to create Riemann Sum animation.
I think just read the wikipedia show it to yourself, if NASA uses it, and you have interest the same as NASA then you probably will try to use JULIA. or perhaps you have interest the same as Federal Reserve and they use Julia too, so you are curious and want to learn what Federal Reserve do with Julia? To print more money and make dollar weaker than CNY?
This talk might suit you or, whole or pieces. It focuses on ground-up Julia programming and how Julia has the dynamism of Python, the numerics of Matlab, and the speed of C, C++, and Fortran. There’s a benchmark of a 1d PDE algorithm coded in C, C++, Fortran, Matlab, Python, and Julia, with comparisons in run-time speed and line count.
It likely needs updating to Julia 1.8, and the PDE benchmark is perhaps not as dramatic as it could be, since the dominant cost of the FFT is the same in all languages. But I’ve found that scientific programming people who struggle daily with C/Fortran and R/Python/Matlab react with “OMG, Julia looks amazing.”
I gave a talk pitching Julia to my community (quantitative ecologists and fisheries scientists, mostly users or R and ADMB/TMB) a couple of years ago that you could look to for inspiration. It got a good response, generated some interest, and earned me a reputation as “the Julia guy,” but I don’t know of anyone who has switched since then.
There are a lot of great reasons to use Julia, but most people will not be motivated to learn a new language until they see a really compelling tool or application that would make their life genuinely easier, but isn’t available in their current language. There’s no harm in giving a presentation to let people know about Julia and its advantages, just be patient and understanding of why people use the tools they do.