My Journey and Lessons Learned

Zachary Sunberg, Assistant Professor

University of Colorado Boulder

Scientific computing is pretty good these days

How many have...

  • Rewritten MATLAB or Python code in C?
     
  • Written Python or MATLAB bindings for C code so that others can use it?

Julia solves the two language problem!!

Julia is

  • as easy to read, write, maintain, and collaborate with as Python or MATLAB
  • as fast as C

EASIER

FASTER

Be skeptical

Find out for yourself!

My Journey

2011

2013

2014

Also ~2011: Improving TCAS

ACAS X

POMDP Models

+

=

Optimization

Specification

Previous C++ framework: APPL

"At the moment, the three packages are independent. Maybe one day they will be merged in a single coherent framework."

JuliaPOMDP

POMDPs.jl - An interface for defining and solving MDPs and POMDPs in Julia

[Egorov, Sunberg, et al., 2017]

POMCP Tree Vis

POMCPOW + Friends

Future

Short Term (next month 🙂)

Long Term

  • New classes of problems (games)
  • Composability (dynamic decision diagrams)

Lessons

1. Composability

Think about 3 People

1. You (package writer)

2. User

3. Other Package Writer

# HybridAStar.jl

Environment(xlim, ylim, obstacles)
# Polygons.jl

Base.in(x::AbstractVector, p::Polygon)
if collision(state, obstacle)
    # ...
end
if state[1:2] in obstacle
    # ...
end

2. Make the easy things easy

Old:

Tip for Julia: Let the user pass functions that define behavior

(they can make custom types <: Function if they want)

transition!(m::YourMDP, s::State, a::Action)

New:

transition(m::YourMDP, s, a)

(And the complex things possible)

Old:

value_estimate = SquaredStateValueEstimator()

New:

estimate_value = s->s^2

3. Communication (simplicity) is key

Old:

transition!(m::YourMDP, s, a, d=create_transition_distribution(m))

New:

transition(m::YourMDP, s, a)

Old:

generate_s(m::YourMDP, s, a, rng) = s + a + randn(rng)

New:

transition(m::YourMDP, s, a) = ImplicitDistribution(rng->s+a+randn(rng))

Thank You!

zachary.sunberg.net

Why should I believe it's fast?

Julia - Speed

Celeste Project

1.54 Petaflops

Why should I believe it's easy to use?

How does it work?

Speed: Just-in-time LLVM Compilation

Code: Multiple Dispatch,

Optional Type Indications,

First Class Arrays

+

=

Julia is

  • as easy to read, write, maintain, and collaborate with as Python or MATLAB
  • as fast as C

EASIER

FASTER

Cool things that Julia can do

  • Auto-differentiation
  • CUDA
  • Multithreading
  • Distributed Computing, from single chip to a cluster
using CUDA

x_d = CUDA.fill(1.0f0, N)  # a vector stored on the GPU filled with 1.0 (Float32)
y_d = CUDA.fill(2.0f0, N)  # a vector stored on the GPU filled with 2.0

function add_broadcast!(y, x)
    CUDA.@sync y .+= x
    return
end

add_broadcast!(y_d, x_d)
numblocks = ceil(Int, length(y)/256)
CUDA.@sync begin
    @cuda threads=256 blocks=numblocks gpu_add!(y, x)
end

Compatibility with other languages: C

#include <julia.h>
JULIA_DEFINE_FAST_TLS() // only define this once, in an executable (not in a shared library) if you want fast code.

int main(int argc, char *argv[])
{
    /* required: setup the Julia context */
    jl_init();

    /* run Julia commands */
    jl_eval_string("print(sqrt(2.0))");

    /* strongly recommended: notify Julia that the
         program is about to terminate. this allows
         Julia time to cleanup pending write requests
         and run all finalizers
    */
    jl_atexit_hook(0);
    return 0;
}
x=ccall((:mean,"libmean"),Float64,(Float64,Float64),2.0,5.0)
println(x)
3.5
double mean(double a, double b) {
  return (a+b) / 2;
}

Julia from C

C from Julia

Compatibility with other languages: Python

Questions?

zachary.sunberg@colorado.edu

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