4 ms·
My company (a hedge fund) has been using Julia for our major data/numeric pipelines for 4 years. It's been great. Very easy to translate math/algorithms into co
by SatvikBeri 1y ago
My company (a hedge fund) has been using Julia for our major data/numeric pipelines for 4 years. It's been great. Very easy to translate math/algorithms into code, lots of syntactical niceties, parallelism/concurrency is easy, macros for the very rare cases you need them. It's easy to get high performance and possible to get extremely high performance.
It does have some well-known issues (like slow startup/compilation time) but if you're using it for long-running data pipelines it's great.
- nextos 1y agoWhat kind of library stack do you use? Julia has lots of interesting niche libraries for online inference, e.g. Gen.jl, which can be quite relevant for a hedge fund. If you can't talk about library stacks, it'd be at least interesting to hear your thoughts about how you minimize memory allocation.
- SatvikBeri 1y agoVery little actually, we try to minimize dependencies, especially for the core inference engine. We just have some basic stuff like Statistics and LinearAlgebra. We use a lot more libraries for offline analysis, but even there it's just popular stuff like DataFrames combined with our own code. We control memory allocation the boring, manual way – we preallocate all our arrays, and then just modify them, so that we have very little continued allocation in production.