By: Andrew Rosemberg, Chris Davis, Glenn Moynihan, Matt Brzezinski, and Will Tebbutt
Re-posted from: https://invenia.github.io/blog/2020/08/12/juliacon/
We at Invenia are heavy users of Julia, and are proud to once again have been a part of this year’s JuliaCon. This was the first year the conference was fully online, with about 10,000 registrations and 26,000 people tuning in. Besides being sponsors of the conference, Invenia also had several team members attending, helping host sessions, and presenting some of their work.
This year we had five presentations: “Design documents are great, here’s why you should consider one”, by Matt Brzezinski; “ChainRules.jl”, by Lyndon White; “HydroPowerModels.jl: Impacts of Network Simplifications”, by Andrew Rosemberg; “Convolutional Conditional Neural Processes in Flux”, by Wessel Bruinsma; and “Fast Gaussian processes for time series”, by Will Tebbutt.
JuliaCon always brings some really exciting work, and this year it was no different. We are eager to share some of our highlights.
JuliaCon is not just about research
There were a lot of good talks and workshops at JuliaCon this year, but one which stood out was “Building microservices and applications in Julia”, by Jacob Quinn. This workshop was about creating a music album management microservice, and provided useful information for both beginners and more experienced users. Jacob explained how to define the architectural layers, solving common problems such as authentication and caching, as well as deploying the service to Google Cloud Platform.
A very interesting aspect of the talk was that it exposed Julia users to the field of software engineering. JuliaCon usually has a heavy emphasis on academic and research-focused talks, so it was nice to see the growth of a less represented field within the community. There were a few other software engineering related talks, but having a hands-on practical approach is a great way to showcase a different approach to architecting code.
Among the other software engineering talks and posters, we can highlight “Reproducible environments with Singularity”, by Steffen Ridderbusch; the aforementioned “Design documents are great, here’s why you should consider one”, by Matt Brzezinski; “Dispatching Design Patterns”, by Aaron Christianson; and “Fantastic beasts and how to show them”, by Joris Kraak.
But it stays strong in the machine learning community
The conference kicked off with a brief and fun session on work related to Gaussian processes, including our own Will Tebbutt who talked about TemporalGPs.jl, which provides fast inference for certain types of GP models for time series, as well as Théo Galy-Fajou’s talk on KernelFunctions.jl. Although there was no explicit talk on the topic, there were productive discussions about the move towards a common set of abstractions provided by AbstractGPs.jl.
It was also great to see so many people at the Probabilistic Programming Bird of a Feather, and it feels like there is a proper community in Julia working on various approaches to problems in Probabilistic Programming. There were discussions around helpful abstractions, and whether there are common ones that can be more widely shared between projects. A commitment was made to having monthly discussions aimed at understanding how the wider community is approaching Probabilistic Programming.
Another interesting area that ties into both our work on ChainRules.jl, the AD ecosystem and the Probabilistic Programming world, is Keno Fischer’s work. He has been working on improving the degree to which you can manipulate the compiler and changing the points at which you can inject additional compiler passes. This intends to mitigate the type-inference issues that plague Cassette.jl and IRTools.jl. Those issues lead to problems in Zygote.jl (and other tools). We expect great things from changes to how compiler pass injection works with the compiler’s usual optimisation passes.
Finally, Chris Elrod’s work on LoopVectorization.jl is very exciting for performance. His talk contained an interesting example involving Automatic Differentiation (AD), and we’re hoping to help him integrate this insight into ChainRules.jl in the upcoming months.
As well as in the engineering community
This year we saw a significant number of projects on direct applications to engineering, including interesting work on steel truss design and structural engineering. Part of why the engineering community is fond of Julia is the type structure paired with multiple dispatch, which allows developers to easily extend types and functions from other packages, and build complex frameworks in a Lego-like manner.
A direct application of Julia in engineering that leverages the existing ecosystem is HydroPowerModels.jl, developed by our own Andrew Rosemberg. HydroPowerModels.jl is a tool for planning and simulating the operation of hydro-dominated power systems. It builds on three main dependencies (PowerModels.jl, SDDP.jl, and JuMP.jl) to efficiently construct and solve the desired problem.
The pipeline for HydroPowerModels.jl uses PowerModels.jl—a package for parsing system data and modeling optimal power flow (OPF) problems—to build the OPF problem as a JuMP.jl model. Then the model is modified in JuMP.jl to receive the appropriate hydro variables and constraints. Lastly, it is passed to SDDP.jl, which builds the multistage problem and provides a solution algorithm (SDDP) to solve it.
There were several tools for working with networks and graphs
As a company that works on problems related to electricity grids, new developments on how to deal with networks and graphs are always interesting. Several talks this year featured useful new tools.
GeometricFlux.jl adds to Flux.jl the capability to perform deep learning on graph-structured data. This area of research is opening up new opportunities in diverse applications such as social network analysis, protein folding, and natural language processing. GeometricFlux.jl defines several types of graph-convolutional layers. Also of particular interest is the ability to define a FeaturedGraph, where you specify not just the structure of the graph, but can also provide feature vectors for individual nodes and edges.
Practical applications of networks were shown in talks on economics and energy systems.
Work done by the Federal Reserve Bank of New York on Estimation of Macroeconomic Models showed how Julia is being applied to speed up calculations on equilibrium models, which are a classic way of simulating the interconnections in the economy and how interventions such as policy changes can have rippling impacts through the system. Similarly, work by the National Renewable Energy Laboratory (NREL) on Intertwined Economic and Energy Analysis using Julia demonstrated equilibrium models that couple economic and energy systems.
Quite a few talks dealt specifically with power networks. These systems can be computationally challenging to model, particularly when considering the complexity of actual large-scale power grids and not simple test cases. NetworkDynamics.jl allows for modelling dynamic systems on networks, by bridging existing work in LightGraphs.jl and DifferentialEquations.jl. This has, in turn, been used to help build PowerDynamics.jl. Approaches to speed up power simulations were discussed in A Parallel Time-Domain Power System Simulation Toolbox in Julia. Finally, another talk by NREL on a Crash Course in Energy Systems Modeling & Analysis with Julia showed off a collection of packages for power simulations they are developed.
This year the whole event happened online
It may not have been the JuliaCon we envisioned, but the organisers this year did an incredible job in adjusting to extraordinary circumstances and hosting an entirely virtual conference.
A distinct silver lining in moving online is that attendance was free, which opened the conference up to a much larger community. The boost in attendance no doubt increased the engagement with contributors to the Julia project and provided presenters with a much wider audience than would otherwise be possible in a lecture hall.
Even with the usual initialization issues with conference calls (“Can you hear me now?”), the technical set-up of the conference was superb. In previous years, JuliaCon had the talks swiftly available on YouTube and this year they outdid themselves by simultaneously live-streaming multiple tracks. Being able to pause and rewind live talks and switch between tracks without leaving the room made for a convenient viewing experience. The Discord forum also proved great for interacting with others and for asking questions in a manner that may have appealed to the more shy audience members.
Perhaps the most pivotal, yet inconspicuous, benefit of hosting JuliaCon online is the considerably reduced carbon footprint. Restricted international movement has brought to light the travel industry’s impact on the planet and international conferences have their role to play. Maybe the time has come for communities that are underpinned by strong social and scientific principles, like the Julia community, to make the reduction of emissions an explicit priority in future gatherings.
In spite of JuliaCon’s overall success, there are still kinks to iron out in the online conference experience: the digital interface makes it difficult to spontaneously engage with other participants, which tends to be one of the main reasons to attend conferences in the first place, and the lack of “water cooler”-talk (although Gather.Town certainly helped in providing a similar experience) means missed connections and opportunities for ideas to cross-pollinate. Not for a lack of trying, JuliaCon seemed to miss an atmosphere that can only be captured by being in the same physical space as the community. We don’t doubt that the online experience will improve in the future one way or the other, but JuliaCon certainly hit the ground running.
We look forward to seeing what awaits for JuliaCon 2021, and we’ll surely be part of it once more, however it happens.