Parting thoughts about Programming and Programming Languages

Class 

  • Most time spent writing code

     
  • Working in teams of 5
     
  • Code lifetime - 3 weeks

Industry

  • Most time spent reading, understanding, maintaining code
     
  • Working in teams of 100s
     
  • Code lifetime - many years

1. Study the art of programming

  • You are writing code to be maintained by people
  • What makes some code easy to read, maintain, and improve?

http://project.cyberpunk.ru/lib/in_the_beginning_was_the_command_line/

2. Use the right tools for the job

  • Be greedy
  • Don't be afraid to try new tools
  • You don't need a class to teach you a new tool
  • Version control (git)
  • Testing
  • The right programming language

... awesome

... showing its age

  • extremely slow if not vectorized
  • closed source
    • License costs money
    • Limited innovation
    • Limited integration
    • Limited dev tools
  • extremely limited ability to scale to large projects
    • Functions are the only widely-used encapsulation method
    • Everything tied to the path
    • No package manager

Matlab is...

  • Simple
  • Mathematical
  • Has amazing tools
    • ode45
    • fmincon
    • simulink
    • linear systems toolbox

Programming Languages

  • Performant
  • Flexible and Composable
  • Free and Open
  • Easy for a wide range of people to use (for homework)
  • Easy for a wide range of people to understand

C++, Fortran

Python

Python, Matlab

Python, Matlab

C++, Fortran

C++, Fortran

Python

Fast

Easy

2011

2013

We love [Matlab, Lisp, Python, Ruby, Perl, Mathematica, and C]; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off.

We are greedy: we want more.

2012

Programming Languages

  • Performant
  • Flexible and Composable
  • Free and Open
  • Easy for a wide range of people to use (for homework)
  • Easy for a wide range of people to understand

C++, Fortran

Python

Python, Matlab

Python, Matlab

C++, Fortran

C++, Fortran

Python

Fast

Easy

Speed: Just-in-time LLVM Compilation

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

Julia and other open-source languages are out there waiting!

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!

Also ~2011: Improving TCAS

ACAS X

POMDP Models

+

=

Optimization

Specification

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

[Egorov, Sunberg, et al., 2017]

Why should I believe it's fast?

Julia - Speed

Celeste Project

1.54 Petaflops

Why should I believe it's easy to use?

Previous C++ framework: APPL

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

How does it work?

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

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