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+Title: What I Learned At JuliaCon
+Author: bnewbold
+Date: 2016-07-12
+Tags: tech, recurse, julia
+
+*Note: It looks like videos of the JuliaCon talks were uploaded [to
+Youtube][youtube] the day this post was finally published!*
+
+[youtube]: https://www.youtube.com/playlist?list=PLP8iPy9hna6SQPwZUDtAM59-wPzCPyD_S
+
+I was in Cambridge, MA for a few days the other week at [JuliaCon][], a small
+conference for the Julia programming language. Julia is a young language
+(started around 2014 and currently pre-1.0) oriented towards fast numerical
+computation: matrix manipulation, simulation, optimization, signal analysis,
+etc. I've done a fair amount of such programming over the years, and it has
+never felt as elegant or coherent as it could be. The available tools and
+languages are generally either:
+
+[JuliaCon]: http://juliacon.org
+
+<div class="sidebar">
+<img src="/static/fig/julia_logo.png" width="180px" alt="julia logo" />
+</div>
+
+1. stuck in the 1980s in terms of programming language features for safety,
+   productivity, and collaboration (eg, Fortran and Matlab)
+1. expensive proprietary closed-source packages (eg, Matlab and Mathematica)
+1. general-purpose languages with numerical features either hacked on or in the
+   form of libraries (eg, Python)
+
+There is a lot to be excited about in Julia. It's already pretty fast
+(leveraging pre-existing JIT tools, hand-tuned matrix and solver libraries, and
+the LLVM compiler suite) and has contemporary high-level language features
+(like optional type annotation, polymorphic function dispatch, package
+management tools, and general systems tools (eg, JSON and HTTP support)) that
+can make the language more faster to develop in, and easier to read and
+maintain. I'm personally excited about the progeny of the language: the
+birthplace of the language is the CSAIL building at MIT, and the spirit of
+[Scheme][sicm] and the work of [Project MAC][] is sprinkled through the
+project. One of the [big pitches](graydon2) of Julia is that scientists won't
+need to learn both a productive high-level language (eg, Python) and a
+low-level performant language (eg, C or Fortran) and interface between the two:
+Julia has everything all in one place.
+
+[graydon2]: http://graydon2.dreamwidth.org/3186.html
+[sicm]: https://mitpress.mit.edu/sites/default/files/titles/content/sicm/book.html
+[Project MAC]: http://groups.csail.mit.edu/mac/projects/mac/
+
+All that being said, while I thought I would be working in Julia a lot during
+my time at the Recurse Center, I've ended up being much more drawn to the
+[Rust][] language instead. Rust is a general systems language (it's compiled,
+has stronger typing, and no garbage collection), and not great for interactive
+numerical exploration, but I've found it a joy to program in: for the most part
+everything *just works* the way it says it will. My recent experience with
+Julia, on the other hand, has been a lot of breakage between library and
+interpreter versions, poor developer usability (eg, hard to figure out where
+files should live in a package), and very frustrating import/load times. Though
+I have to admit that I while I pushed through some frustrations with Rust, I
+haven't spent *that* much time with Julia, and may have just been impatient, so
+take everything I say here with a grain of salt.
+
+With these feels going in, what did I learn at JuliaCon and what do I think of
+the future of the language now? In the below sections I'll go over the
+interesting things I saw, then come back to summary at [the end](#summary).
+
+### Programming Language Design
+
+An older research language for numerical computing that I have always been
+curious about is Fortress, and the leader of that project (Guy Steele, who also
+worked on the design of the Scheme and Java languages) gave one of the opening
+keynote speeches at JuliaCon this year.  Awesome! I get really excited about
+inter-generational learning and dialog.
+
+Fortress was a very "mathy" language. The number tower was intended to be
+"correct" (aka, have the same structure that mathematicians use), physical
+units were built-in, and some operator precedence was non-transitive. Operators
+on built-in types (like Integers) could be overloaded, unlike in Java, because
+Fortress users could apparently be trusted to "preserve algebraic properties".
+Steele is a proponent of using whitespace (or lack of whitespace) to clarify
+expressions, sort of like extra parentheses, and enforcing this in the
+compiler. For example, the following two statements would be equivalent in most
+languages, but not in Fortress:
+
+```
+a + b*c + d     // Clear: Ok
+a+b * c+d       // Misleading: Compiler Error
+```
+
+This was part of a general effort to allow "whiteboard" style syntax in the
+language. Fortress code actually has two representations: a plain text
+Scala-style source code, and a LaTeX-y symbolic math format. Steele also used
+some font-coloring in his slides to differentiate different types of symbols,
+which reminded me of the helpful style my undergraduate physics professors
+would use on the blackboard. I think this effort to adapt the "look and feel"
+of the language to how the intended audience already writes and communicates is
+really cool. I wonder if a third syntax format could have been added in a
+one-to-one manner: that of a general purpose language like Scala or Haskell
+(both noted as influences to Fortress) to make collaboration with general
+purpose programming experts easier. Steele mentioned that some efforts to make
+the syntax more math-like resulted in "contortions", so there is probably more
+work to be done here.
+
+In my limited experience, Julia has a pretty clean syntax, and allows some
+math-y [unicode characters as operators][unicode_ops] (like ∈, ≠, etc), but
+didn't prioritize math-y syntax as much as Fortress. Given the open challenges
+with formalizing informal whiteboard syntax this may or may not have been a
+missed opportunity.
+
+[unicode_ops]: http://docs.julialang.org/en/release-0.4/manual/unicode-input/
+
+The positive lessons learned from Fortress were summarized as being the type
+system, automatic parallelism (via generators and reducers), the math-y syntax,
+pretty printing (I assume meaning the LaTeX-y representation), physical units,
+and forced syntax clarity (aka, forced use of parentheses and whitespace). One
+issue that come up during implementation was that it was hard to bound the
+latency and computational complexity of type constraint solving at run-time.
+
+A few other talks touched on language design decisions and features. There was
+a short "Functional HPC" talk by Erik Schnetter, in which it was pointed out
+that for some workloads regular old garbage collection can be faster than
+reference counting: I've become used to thinking of latency and GC pauses as a
+huge performance problem in systems programming, but for number crunching that
+isn't as much of an issue, while little reference overheads are (especially if
+locks or atomic operations are necessary).
+
+Keno Fischer gave an overview of the [Gallium][] debugger, which had some cool
+features, but is still under development. There are both AST-based and
+LLVM-based backends for the debugger, which allows stepping at function calls,
+line-by-line, or expression-by-expression, which is something I hadn't seen
+before. He demoed stepping through each step of the creation of a matplotlib
+graph, with the output shown graphically after each step. Neat stuff!
+
+One of my personal interests in Julia would be formalizing the syntax into a
+machine-readable grammar (eg, [EBNF][] or [ABNF][]). I was lucky enough to run
+in to Stefan Karpinski during one of the coffee breaks, and he pointed me to
+the Julia plugin for Eclipse, which already has a partial implementation of a
+grammar.
+
+A few talks touched on the issue of Nullable datatypes (also called "Maybe" or
+"Option" types in other languages), particularly for data science and
+DataFrame-type applications. I only recently encountered [Option][] (and the
+related [Result][] type) datatypes, in Rust, and can see why people want these
+so badly, but there doesn't seem to be a simple path forward yet. Rust really
+leverages these types in function return signatures, a feature which Julia does
+not have for now; I think I read rumors about them being added in the future,
+but didn't hear any mention of them here or on the 1.0 feature roadmap.
+
+[Option]: https://doc.rust-lang.org/std/option/index.html
+[Result]: https://doc.rust-lang.org/core/result/index.html
+[Gallium]: http://juliacon.org/abstracts.html#Gallium
+[EBNF]: https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_Form
+[ABNF]: https://en.wikipedia.org/wiki/Augmented_Backus%E2%80%93Naur_Form
+
+### Numeric Abstraction
+
+One of the big trends I saw was taking advantage of Julia's abstractions around
+generic operators and arrays to experiment with novel computation strategies.
+Sometimes this means improving precision (with novel data types and
+representations), sometimes it means increasing performance (by changing memory
+layout or distribution, or targeting special hardware), and sometimes it just
+makes code more elegant or semantic.
+
+For example, Tim Holy gave a talk (titled "To the Curious Incident of the CPU
+in the run-time") which covered a bunch of nitty-gritty details for
+implementing wrapper classes that re-shape or re-size Arrays, including sparse
+arrays.
+
+Lindsey Kuper gave a nice overview of the [ParallelAccelerator.jl][pajl]
+project, which entirely re-compiles Julia into C++ to get some extra performance
+from the static full-program compiler. It seems to me that this only makes
+sense because the Julia language has clean abstractions that the transpiler can
+take advantage of.
+
+[pajl]: http://juliacon.org/abstracts.html#ParallelAccelerator
+
+One of my favorite talks from the whole conference was David Sanders' and Luis
+Benet's talk on ValidatedNumerics ("Precise and rigorous calculations for
+dynamical systems"). Instead of computing on approximate (rounded) scalars,
+they compute on intervals of floating point numbers (or in higher dimensions,
+boxes): at the end of computation the "correct" solution is known to be within
+the final box, which also gives context as to how much numerical error has
+accumulated. By defining new *types* to accomplish this (specifically,
+DualNumbers), they can re-use any generic code in a relatively performant
+manner. They also noted that when there is an analytic form to bound the error
+for all following terms, Taylor expansion approximations can be truncated as
+soon as the interval error exceeds the error in all following terms. Cool!
+
+### Other Fun Stuff
+
+**[Using Julia as a Quick and Dirty Code Generator][10]:**
+The speaker (Arch Robison) is clearly having way too much fun! He used Julia to
+output assembly code to get fast (real-time) discrete Fourier transform (DFT)
+performance for a little video game called "FreqonInvaders". Infectious
+enthusiasm!
+
+**[Autonomous driving for RC cars with ROS and Julia][11]:**
+A fun little project doing "Model Predictive Control" on a small model car to
+do stunts like drifting and slide parking into a tiny space. They achieved
+about a 10Hz closed-loop control latency, which seems to me like barely enough
+for this sort of thing, but clearly worked alright. Everything ran on the car
+itself (no computation on a remote desktop with wireless control or anything
+like that), with an Odroid ARM Linux system and an Arduino-compatible
+microcontroller; Julia code using JuMP and other optimization stuff ran on the
+ARM system. The code and raw data (for analysis) is available on the [BARC
+project website](http://www.barc-project.com). Super cool, having this stuff
+being experimented with already means there will be pressure to improve
+soft-real-time performance in the language itself.
+
+**[Astrodynamics.jl: Modern Spaceflight Dynamics in Julia][12]:**
+Mostly a bunch of code for doing timebase conversions and interpreting (or
+calculating) ephemeris data (which is information about where astro bodies like
+the Moon and planets will be at a given time), but some simple demos of orbital
+simulation and event detection (eg, perihelion time and position) as well. Would
+be cool if the ValidatedNumerics stuff was integrated.
+
+**[GLVisualize][13]:**
+The demos in this talk were really impressive: live editing of mesh vertices,
+relatively high performance, real-time feedback, etc. There were a bunch of
+good graphics talks: the [GR Framework][14] stuff is really impressive in scope
+(though maybe not as big a performance boost over Python as hoped), and
+[Vulkan][15] is exciting.
+
+[10]: http://juliacon.org/abstracts.html#FrequonInvaders
+[11]: http://juliacon.org/abstracts.html#RaceCars
+[12]: http://juliacon.org/abstracts.html#Astrodynamics
+[13]: http://juliacon.org/abstracts.html#GLVisualize
+[14]: http://juliacon.org/abstracts.html#GR
+[15]: http://juliacon.org/abstracts.html#Vulkan
+
+### Diversity
+
+It's sad to say, but the gender diversity at the conference was really poor,
+particularly in contrast to the Recurse Center (where I have spent the past
+couple months). The women I did meet gave some of the best talks, are crucial
+contributors to infrastructure, and are generally amazing: more please! Aside
+from the principle of the thing, there is just something about a giant sea of
+guys at a tech event that results in a tense group vibe. Everybody I spoke to
+one-on-one was friendly and we had great conversations, but as a group there
+was a lot of ice to be broken. In my experience even hitting 10-20% women in
+attendance can thaw this out, but that's just my anecdotal experience.
+
+I haven't attended, but I hear that PyCon has done a great job improving
+diversity with careful planning and [systemic initiatives][pycon-diversity].
+
+Overall, I thought the conference was a great group of people and admirably
+well run. I appreciated the efforts to keep costs low, and everything generally
+ran on time. Thanks to all the volunteer and MIT staff organizers for their
+efforts!
+
+[pycon-diversity]: https://us.pycon.org/2016/about/diversity/
+
+### Julia 1.0
+
+Stefan Karpinski gave an overview of features and roadmap for getting to Julia
+1.0, which I think was a topic close to most attendee's hearts (including
+mine). I ended up with a huge list of written notes, which I'll summarize
+below; the punchline was aiming to have a 1.0 release around one year from now.
+Apparently the one-year goal has been floated in previous years; I'm not sure
+how wise it is in general to float initial release timelines for a project like
+this, it seems like it will just "be done when it's done".
+
+Some of the goals that were interesting to me:
+
+- Arrays: might refactor Arrays to have a separate backing abstraction of "Buffers"
+  with arrays on top (apparently Lua and Torch do this).
+- Strings: move full Unicode support out of core language (Base) and into a
+  package. The `@printf` macro will be refactored into a function. To my
+  surprise, currently Strings are implemented as an Array! This has a
+  relatively large overhead for each string (72 bytes).
+- Modularity and Package infrastructure: currently a mess (I agree), `import`,
+ `using` and `export` will be refactored.
+- Compiler: add non-pthreads multithreading; better static compilation; ability
+  to define a `main()` function and get a standalone script or binary; ability
+  to redefine functions and have the changes propagate (cache invalidation
+  problem); stabilize intermediate representations. Seems like a lot!
+- Optimizations: faster garbage collection, more auto-vectorization (eg, for
+  vector floating point units), improve globals performance. Might pull in part
+  of ParallelAccelerator?
+
+I'm a little nervous how many of these goals are big open questions instead of
+just implementation tasks. I wish there was a more healthy way to experiment
+with new features and refactoring without breaking everything or committing to a
+long-term stable API; I think other languages have settled into good patterns
+for this kind of development, though maybe they needed to go through a
+difficult 1.0 process first. It was mentioned that 0.6 would be the last of the
+0.x series of releases and considered 1.0-alpha, and that from 1.x and on
+things should generally be backwards compatible.
+
+Separate from Stefan's talk, there was a short overview of progress on the next
+iteration of the Julia package and dependency manger, called Pkg3. The goals
+were described as "a mash-up of virtualenv and cargo": virtualenv is a tool for
+isolating per-application dependencies and toolchains in Python, and Cargo is
+is the Rust dependency manager and build tool (which is also used in a
+per-application fashion). Pkg3 sounds like it will have a concept of distinct
+"global" (meaning system-wide?) installations and "local" (eg, per-project or
+per-directory) installations and name-spacing. The naming could use some work,
+as "global" and "local" are pretty overloaded, but I think they are chasing the
+right goals. Reproducibility (both for binary generation and data/experiment
+reproduction), lock files (which lock in known-good versions of dependencies a
+la Cargo), and other concepts that I care about were also thrown around. I
+didn't catch all the details (and I'm not sure how much has been worked out and
+implemented yet), but after my experiences with [Elm and Rust][elm-broken], and
+the current state of packaging for Julia, I'm excited for Pkg3!
+
+[elm-broken]: /2016/elm-everything-broken/
+
+<a name="summary"></a>
+
+### Overall Julia Feels
+
+There is sort of an explosion of ideas and experiments going on. It feels sort
+of like what the Ruby community maybe went through with web frameworks, or the
+web community did with languages that compile to Javascript: ambitious ideas,
+which may have been on the back-burner for some time, can finally be prototyped
+quickly and tested in a mostly-real-world environment, and everybody is excited
+to try it out and demo their creations.
+
+One of the sponsors said:
+
+> "there is something quite good about not feeling bad about programming"
+
+and that seemed representative of the current state of Julia. It seems
+undeniable that the language is less painful for developing performant
+numerical code than the previous generation of languages and library wrappers.
+
+Perhaps because of this enthusiasm and froth of ideas, I'm a little worried
+that the foundations of Julia (the language and the ecosystem) have not yet had
+time to fully bake. The more demos and experiments that get implemented, and
+the more popular they become, the more delicate it becomes to make hard
+decisions about language syntax and features. I think people want stability and
+promised features *yesterday*, but these things take time and reflection. My
+feelings right now is that it doesn't really matter. The enthusiasm for
+*a language like Julia* is proven and growing. Julia itself might end up being
+the first try that gets thrown away in a decade or two, but in the end we'll
+end up with something which is both exciting and robust.
+
+[PyX.jl]: https://github.com/bnewbold/PyX.jl
+[rust]: https://www.rust-lang.org/
+
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