A General Presentation on Julia Computing - a language which is mainly used for numerical analysis & is thus, a more efficient alternative to Hadoop

1. Julia Computing
Shaurya Shekhar (14BCE0497)
Aarushi Thakral (14BCE0499)

2. • 4 Main Creators - one of which
is Indian - Viral B. Shah.
• He started working on it on the
same day as he joined UIDAI -
the worlds largest biometric
collection and collation
initiative - Aadhaar by Day -
Julia By Night
• MIT Professors played
instrumental role

3. The Need For Julia
MATLAB for matrix
calculations and linear
algebra
R Language for
statistics
Ruby & Python for web
development
All Languages Serve Different Purposes
However, they aren’t as fast as C or Java.
started off as a network simulation tool.
It was meant to be ‘good at EVERYTHING’.

4. What Is Julia?
• Data Science is nowadays a very big deal.
• It involves tonnes of data and the analysis of this
data to evolve meaningful inferences.
• Most of the softwares which help in these are
proprietary like MATLAB and Wolfram’s
Mathematica.
• is the first free alternative.

5. How Is It Faster?
THE PROBLEM
• Programmers use tools to
translate languages like Ruby
& Python into faster languages
like C & Java.
• This in turn then needs to be
compiled into machine code -
language that the machine
understands.
• This makes it slower, adds
complexity and allows more
room for error
Solution
Eliminates the need of
the intermediary step.
Uses LLVM, a
compiler developed
by UI-UC and
enhanced by Apple &
Google

6. How Does It Compare?

7. Alternative to Hadoop
• Hadoop is the widely used data crunching system
developed by Yahoo and used by Facebook.
• Hadoop breaks up a larger problem into many
smaller problems, spreads them across many
systems.
• Julia not only incorporates this fundamental
principle of ‘design parallelism’, but also enhances
it.

8. Features
• Multiple Dispatch
• Dynamic Programming Language
• Good Implementation Speed
• Metaprogramming
• Built-in Package Manager
• Designed for Parallelism & Cloud Computing

9. Features (Cont.)
• User-Defined Data Types as fast as built in ones
• Elegant & Extensible Conversions
• MIT-licensed (free & open-source)

10. Multiple Dispatch
• We All Know What Polymorphism is about.
• Number & type of arguments have to be analyzed
to understand which of the function
implementations needs to be executed.
• This is different from what we’ve learnt in C++, as in
C++, the switch is made during compile time,
whereas here it is made at run-time.

11. Dynamic Programming
Language
• It is a term used in computer science to describe a
class of high-level programming language, which
at run-time, execute many common programming
behaviors that static programming languages
perform during compilation
• First native in the Lisp language

12. Good Implementation
Speed
• Almost at most times equivalent to C (the grand-
daddy of programming languages)
• The secret behind the highly efficient Julia
computing is:
1. Just-In-Time (JIT) Compilation using LLVM
Compiler Framework
2. Language Design

13. LLVM
(Low Level Virtual Machine)
• It is a collection of modular and reusable compiler and
toolchain technologies
• Written in C++
• Can generate relocatable machine code at compile-time or
link-time or even binary machine code at runtime
• It supports language independent instruction set & type system
• Each instruction is in static single assessment form (SSA),
which means that each variable is assigned once and is frozen

14. Metaprogramming
• It is the ability to write programming languages which
treat their programs as their data. The program could
be designed to read, generate, analyze or transform
other programs & even modify itself while running
• Hence technically, the program operates on code
itself, this involves inspecting & modifying the code
as it runs
• The strongest legacy of Lisp in Julia is its
metaprogramming support

15. Built-in Package Manager
• It has a built-in manager for installing add-on functionality.
• All package commands are found in Pkg module & are included with ‘Base’
install itself.
• This ensures that libraries of other languages can be ported easily into Julia.
• Example:
1. ’ccall’ is used to access the C shared libraries
2. It has Unicode support for allowing math operators
3. For Strings, it has UTF-8, UTF-16 & UTF-32 & ASCII
4. Markup Languages Like HTML & XML are also supported

16. Parallelism
• It provides a multiprocessing environment based on
message passing to allow programs to run on multiple
processors in shared or distributed memory
• Implementation of message passing is one-sided
User has to manage only one processor
These do not look like message send & receive,
instead resemble high-level function calls
Two key notions are: remote calls & remote references

17. Remote Calls & Remote
References
• A remote reference is an object that can be used from any processor to refer to
an object stored on a particular processor
• A remote call is a request by a processor to call a certain function on certain
arguments on another (possibly the same) processor. A remote call returns a
remote reference.
• How remote calls are handled in the program flow:
1. Remote Calls return immediately
2. Processor proceeds to next operation while remote call happens
somewhere else
3. You can wait for it to finish by calling ’wait’ on its remote reference
4. You can obtain full value of result by ‘fetch’

18. Conversions
• Conversions of values to various types is carried
about by the ‘convert’ function
• Its a function which accepts two arguments, the
first is a type object, the second is a value to
convert to that type
• It is also really easy to define our own conversions

19. Licences
• The core of Julia implementation is licensed under
the MIT License.
• Various libraries used by Julia have their own
licenses.
• It is an open-source language which gives people
the flexibility of modifying the language to better
suit their needs.

20. Plotting Capabilities
• Since, this is being used to handle large amounts of
data, it is only normal for it to be able to aptly visualize
data easily.
• It uses various libraries to enable it to plot graphs, flow
charts, pie charts like:
1. PyPlot to call Python’s matplotlib from Julia with
little or no overhead (linspace)
2. Gadfly is another implementation of a different style
of grammar of graphics (draw)

21. Similar to MATLAB (using PyPlot)

22. Using Gadfly

23. Just For Fun

24. Lets Take An Example
We Use The N-Queens Problem

27. Understanding Time
Notations

32. Julia In The Future

33. Thank You