In our company, Julia blurs the barrier between prototyping done by data scientists and production development done by our developers. Thanks to Julia, both activities are carried out within the same platform. This brings a lot of advantages, including much faster development and much easier maintenance of code.

Solvency II compliant models in Julia are 1000x faster than Algorithmics, use 10x less code and took 1/10 the time to implement.

I just wanted to thank you for Julia. I am the manager of an engineering group responsible for quite a few numerical tools, from stand-alone small programs to full-blown finite element codes. Julia is the most exciting thing I've seen in years. When a language is cool enough for a 50-something year old manager to spend his spare time programming in it at home, you know that you've kindled serious excitement.

Julia is faster than Python, but more than that it’s much more expressive: the type system, macros and multiple dispatch enable us to tackle more ambitious projects. But it also interoperates with Python, so we can take advantage of our existing libraries and fit into the ecosystem.

Julia is very easy to understand. It’s a very familiar syntax, which helps the reader understand the document with clarity, and it helps the writer develop algorithms that are concise. We continue to push Julia as a standard for specifications in the avionics industry. Julia is the right answer for us and exceeds all our needs.

We chose Julia which gives us the expressiveness and development speed of Python and the performance of C with a language and type system that aids and does not impede productivity.

With Julia, the business side user can build prototypes without having to worry about speeding up code later with C++. It’s very cost effective since there is no need to write glue code to translate R datatypes. Also, the language syntax is even simpler than R. We can write for loops. Goodbye cryptic vectorized code!

Being high level and having an ability to iterate quickly makes a major difference in a fast-paced innovative environment like at Voxel8. The speed at which we’ve been able to develop this has been incredible. If we were doing this in a more traditional language like C or C++, we wouldn’t be nearly as far as we are today with the number of developers we have, and we wouldn’t be able to respond nearly as quickly to customer feedback regarding what features they want.

The Julia code is therefore more than 100 times faster than the equivalent Python code. Multiple dispatch with function calls gives Julia extremely efficient code that is practically superior to any high-level language. Faster code in Julia can be achieved without any tricks like vectorization or outsourcing to C extensions. By contrast, such tricks are often necessary to speed up Python or R code.

Julia is a great tool. We like Julia. We are very excited about Julia because our models are complicated. It’s easy to write the problem down, but it’s hard to solve it – especially if our model is high dimensional. That’s why we need Julia. Figuring out how to solve these problems requires some creativity. This is a walking advertisement for Julia.