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Julia for the Win

In updating a paper to prepare for journal submission I needed to revisit the accompanying Julia code. I chose Julia at the time because this was a mostly self-contained project and I wanted to give Julia a trial run on something of moderate complexity (see first impressions). I cleaned up the code, added some capabilities, and really tried to improve performance. I read all the primary documentation on Julia, including the sections on performance, updated to 0.4.x, explicitly declared all types, and profiled quite a bit. This made some difference, but my code was still about an order of magnitude slower than a Python/Fortran version.

The original version was mostly Fortran so I wasn’t necessarily seeking a speed improvement. For the benefits of working in one language I would be ok with a 20-30% slowdown. However, an order of magnitude slow down was a lot and it really bogged things down when creating figures and data for the paper. I had about given up on performance, but two separate encounters with Julia-using colleagues made me revisit the topic once again. I sent my code to a colleague who had been using Julia for some time. His PhD student pointed out some suggestions, which I tried, but the impact was negligible.

Profiling suggested that a lot of time was spent in conversion between PyCall and scipy. I tried using the lower-level calls in PyCall to be more explicit in the types, but it didn’t help. I was using PyCall because I needed an N-dimensional root solver and nothing was available in native Julia. Roots.jl was available, but it only solves 1-dimensional root finding problems. PyCall allowed me to call Python’s scipy.optimize.root, which was quite convenient.

When I first wrote this code a year or so ago I directly wrapped the Fortran code hybrd from minpack (using Julia 0.2.x and 0.3.x), which is what is used in Scipy’s root finder. I later discovered PyCall and that was way more convenient with no real change in computation time. However, I noted that a lot of improvements were made to ccall and cfunction in 0.4.x so I thought I’d try the direct route again. I reverted to my direct wrapper of hybrd (with a few minor updates to comply with 0.4.x), to eliminate the Python middleman. Performance problem solved! After that change I found that the Julia code was actually 3X faster than the Python/Fortran version!

The other problem I had was that plotting and just running Julia in general was painfully slow. I was using a Python-like workflow where I would just run the full script each time (using Sublime Text or the terminal). I knew that Julia had a REPL, but I couldn’t stand working in the browser as opposed to a text editor. Sometimes with plotting I would use the REPL, but it was a real pain switching back and forth. My colleague informed me about Ink and the Julia Client in Atom and it changed everything. With this tool I could avoid the recompilation of packages and code that before was occurring on every run.

I’m now very happy with using Julia and for one of our new major research projects we are going all in. Everything isn’t perfect. Some libraries are nonexistent (although wrapping Fortran/C libraries is pretty easy), and the Atom Julia Client is still a bit rough around the edges and could use a debugger. But overall, working within one language that is both performant and is very easy to work with is a big win for us.

P.S. If interested in the mentioned code, I have open-sourced it and it is available on GitHub. It computes aerodynamic loading for multiple vertical axis wind turbines using an extended version of actuator cylinder theory. A preprint of the accompanying theory is available on this website under “Actuator Cylinder Theory for Multiple Vertical Axis Wind Turbines”.