the julia technical computing language - an introduction · 15-03-2017 · aviksengupta. april...

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

The Julia Technical ComputingLanguage

An Introduction

Justin [email protected]

Advanced Research ComputingVirginia Tech

March 15, 2017

Intro to Julia An Introduction (March 15, 2017) 1

mailto:[email protected]

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

Overview


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduWhat is Julia?

I Technical computing languageI Lots of built-in functionalityI FastI Free (MIT License)I http://julialang.org/I Entered Top 50 languages by TIOBE index

September 2016


http://julialang.org/http://www.tiobe.com/tiobe-index/

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduAccess

I ARC (e.g. NewRiver)I Load it

$ module load gcc atlas python julia/0.5.0$ which julia/opt/apps/gcc5_2/julia/0.5.0/bin/julia

I Build your own:$ module purge; module load gcc cmake$ make -j

I JuliaBox: http://www.juliabox.orgI Download to your machine (.dmg, .exe)


http://www.juliabox.org

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduLimitations

I New: First released in 2012, Latest version is 0.5.0I Still changing - this error appeared in v0.5:

A = rand (10 ,10); y = zeros(100 ,10);

y[1 ,:] = rand (10);y[2 ,:] = y[1 ,:] * A.’;ERROR: DimensionMismatch ("A has

dimensions (10 ,1) but B hasdimensions (10 ,10)")

I Basic documentation is extensive, but outsidedocumentation can be sparse


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

Julia Basics


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduPackages

I Julia contains a built-in package manager similarto R

I Easy to add and remove third-party packagesI Examples: HDF5 for file I/O, Distributions for

probability distributionsI To add a package: Pkg.add("HDF5")I To update packages: Pkg.update()I To use a package: using HDF5I Package listing: http://pkg.julialang.org


http://pkg.julialang.org

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduPlotting

I Julia does not come with any built-in plottingcapabilities

I However, plotting is easily added with twopackages:

I Gadfly: Based on R’s ggplot2 packagePkg.add(" Gadfly ")using Gadfly

I PyPlot: Uses Python’s matplotlib library.(Requires that Python, Matplotlib be installed.)

Pkg.add(" PyPlot ")using PyPlot

I ARC installations come with these pre-loadedI Plots package provides wrappers


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduPlot Examples

1.0 0.5 0.0 0.5 1.0x

1.0

0.5

0.0

0.5

1.0y

PyPlot Example

x

-1.0 -0.5 0.0 0.5 1.0

-1.0

-0.5

0.0

0.5

1.0

y

Gadfly Example


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduDevelopment and Editing

I Juno: Integrated Development Environment (IDE)for Julia built on Atom editor.

I IJulia: Provides Jupyter notebook forbrowser-based development

I Download julia-vim to add Julia syntax to vimtext editor

I Debugging via the Gallium package


http://junolab.orghttps://atom.iohttps://github.com/JuliaEditorSupport/julia-vim

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduPerformance

Performance is one of the main draws of Julia:I Just-in-time (JIT) Compilation: Compiles at

run-time, when more optimizations are availableI Performance comparable to C

Figure : Run-time / C Run-time


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

Structure


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduComparison with Matlab

Much Matlab syntax is valid in Julia, but there aredifferences:

I Strings with “string” rather than ’string’I Comments are #, not %I Arrays passed by reference (think C)I Array indexing with [i,j], not (i,j)I Types: Integers, tuples, rangesI return statementI Some functions (e.g. max()) operate element-wiseI No need for semicolons at end of statements

See documentation for full list


http://docs.julialang.org/en/release-0.4/manual/noteworthy-differences/#noteworthy-differences-from-matlab

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduComparison with R

Differences are more extensive than for MatlabI Strings with “string” rather than ’string’I Arrays passed by reference (think C)I

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduComparison with Python

I end to end a block (if, for, while, etc)I Array indexing is 1-based (not 0-based)I Arrays are column-major (reorder indices for best

performance)

See documentation for full list


http://docs.julialang.org/en/release-0.4/manual/noteworthy-differences/#noteworthy-differences-from-python

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduTypes

I Integers: Int8, Int16, Int32, Int64, Int128I Floats: Float16, Float32, Float64I Range: rng=0:0.1:1 returns a

FloatRange{Float64}I Many more

Enables speed but can cause headaches, e.g.

function recip(x); return x^(-1); endrecip(2)

yieldsERROR: DomainError: Cannot raise aninteger x to a negative power -n.


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduMultiple Dispatch

Allows multiple functions to be defined with the samename, e.g.:

function recip(x::Int)return float(x)^(-1);

endfunction recip(x::Float64)

return x^(-1);end

Then recip(2) yields 0.5.You can also define functions with optional arguments.


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

Parallel Julia


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduThreaded Linea Algebra

I Julia built with Basic Linear Algebra Subroutines(BLAS). The default is OpenBLAS.

I BLAS are threaded, leveraging multiple cores on asingle computer, so Julia is parallel when doinglarge-scale linear algebra.

I To control the number of threads used, use theOPENBLAS_NUM_THREADS environment variable.export OPENBLAS_NUM_THREADS=24


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduStarting Julia in Parallel

You can also manually start multiple instances of Juliausing julia -p, e.g.

julia -p 24 ’include("script.jl")’

to run the script script.jl on 24 cores.You can also start them on multiple machines:

julia --machinefile hosts.txt’include("script.jl")’


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduUsing Parallel Julia

To use multiple workers, the program must tell theworker processes what to do, e.g.

I Load a script on all processes: @everywhereinclude("count_heads.jl")

I Get a process’s ID: myid()I Push a set of jobs to workers: pmap()I Created a shared array: SharedArray()

There is a lot more. See here and here.


http://docs.julialang.org/en/stable/manual/parallel-computing/http://docs.julialang.org/en/release-0.5/stdlib/parallel/

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduParallel For

If a for loop has independent iterations, it can beconverted into a parallel for loop:

total = @parallel (+) for i=1:n

Iterations are split across all available processes andtotal summed across them to yield the answer.This is similar to MATLAB’s parfor functionality.


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.edu

Conclusions


Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduReferences

I Julia WebsiteI “Julia: A Fresh Approach to Numerical

Computing.” Jeff Bezanson, Alan Edelman, StefanKarpinski, and Viral B. Shah. SIAM Rev., 59(1),65-98. (SIAM, Early version on arXiv)

I “Julia for R Programmers.” Douglas Bates. (link)I “The Julia Express.” Bogumil Kamiński. (link)I “Julia High Performance.” Avik Sengupta. April

26, 2016. (Amazon)


http://julialang.org/http://epubs.siam.org/doi/abs/10.1137/141000671https://arxiv.org/pdf/1411.1607.pdfhttp://www.stat.wisc.edu/~bates/JuliaForRProgrammers.pdfhttps://github.com/bkamins/The-Julia-Expresshttps://www.amazon.com/Julia-High-performance-Avik-Sengupta/dp/1785880918

Intro to Julia

Justin Krometis

Overview

Julia Basics

Structure

Parallel Julia

Conclusions

www.arc.vt.eduQuestions?

Justin [email protected] Research ComputingVirginia Tech


mailto:[email protected]

OverviewJulia BasicsStructureParallel JuliaConclusions

the julia technical computing language - an introduction · 15-03-2017 · aviksengupta. april...

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