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diff --git a/posts/juliacon.md b/posts/juliacon.md deleted file mode 100644 index 52751a9..0000000 --- a/posts/juliacon.md +++ /dev/null @@ -1,339 +0,0 @@ -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/ - |