Rapidly growing technology-HIGH PERFORMANCE COMPUTING .A language to support this is julia which is not known to every one

1. Presented by
D.ANUSHA
G.SAILAJA

2.  Introduction
 Importance of julia
 Features
 Julia with other programming languages
 Sample program on julia
 Advantages
 Disadvantages
 Conclusion
Contents

3. Introduction
 As high-performance computing (HPC) bends to the
needs of "big data" applications, speed remains essential
 The programs are getting more and more complex and
time-consuming to develop. A few years ago, when an
HPC startup Edelman was involved in Interactive
Supercomputing .It was acquired by Microsoft and its
group set out to develop a programming language, julia.

4.  Julia is a high-level, high-performance dynamic
programming language for technical computing, with
syntax that is familiar to users of other technical
computing environments.
 It provides a sophisticated compiler, distributed parallel
execution, numerical accuracy.
Why Julia

5. The creators wanted a language that satisfies:
1.The speed of C.
2. With the dynamism of Ruby.
3. Mathematical notations like Matlab.
4. As usable for general programming as Python.
5.As easy for statistics as R.

6. Multiple dispatch
Good performance
Built-in package manager
Call c functions directly
Parallel computing
Special FEATURES of Julia

7. All arguments are equally responsible to
determine a method.
In single dispatch the calls cat.run("fast")
and cat.run(5) would dispatch to the same
method and it is up to the method to do
different things with the different types of the
second parameter.
 In Julia run(cat, "fast") and run(cat, 5)
dispatch to separate methods.
Multiple dispatch

8.  Julia’s LLVM-based just-in-time (JIT) compiler
combined with the language’s design allow it to
approach and often match the performance of C
 Julia’s ability to compile code that reads like
Python into machine code that performs like C
almost entirely derives from Julia’s ability to
specialize function definitions in this way.
High-Performance JIT Compiler

9. Execution time
Julia with other programming languages

10. Foreign function interfaces to a number of
languages like C and Fortran, C++ (unfortunately
planned only for Julia 0.5), Python, R, Matlab.
This makes it relatively easy to use code in any of
these languages
Built-in package manager

11.  To allow easy use of this existing code, Julia
makes it simple and efficient to call C and
Fortran functions.
 The machine instructions generated by Julia’s
JIT are the same as a native C call would be, so
the resulting overhead is the same as calling a
library function from C code.
Call c functions directly

12. # julia
function mmult(A,B)
(M,N) = size(A);
C = zeros(M,M);
for i=1:M
for j=1:M
for k=1:M
C[i,j] += A[i,k]*B[k,j];
end
end
end
C;
end
// C
#define M 500
void mmult(double A[M][M],double
B[M][M],doubleC[M][M])
{
//double C[M][M];
int i,j,k;
for(i=0; i<M; i++)
for(j=0; j<M; j++){
C[i][j] = 0;
for(k=0; k<M; k++)
C[i][j] += A[i][k]*B[k][j];
}
}
Sample program on julia

13. Development of Julia

14.  Julia already possesses a mature package
ecosystem and can be used as a feature-complete
replacement for R or Python.
 Julia’s compiler is so good that it will make any
piece of code fast – even bad code.
 It's touted as a high-level language, which means
it's easier to learn. It's normally faster to write code
in a high-level language.
Advantages

15.  Julia arrays are 1-indexed, which can really trip
you up sometimes when you're used to Python,
C/++, Java, etc.
 Julia list comprehensions (currently) lack the
ability to use conditionals, unlike Python. One
can do this with for loops and if/else, though, as
normally done.
 Julia dictionaries are hashed differently than
Python dictionaries, which can make them
slower in many cases.
Disadvantages

16. Conclusion
Julia is a flexible dynamic language, appropriate
for scientific and numerical computing
Julia combines the features of many other
programming languages like C, Matlab and Java
etc.
Existence of JIT Compiler in Julia increases the
performance of computuing.

17. http://radar.oreilly.com/2013/10/julias-role-in-data-science.html
http://www.nowozin.net/sebastian/blog/the-julia-language-for-scientific-
computing.html
http://www.evanmiller.org/why-im-betting-on-julia.html
http://julialang.org/
http://docs.julialang.org/en/release-0.4/manual/functions/
References:

18. ANY QUERIES???