- 1. Dhammpal Ramtake SoS in Computer sciences & IT Pt.
R.S.U. Raipur, (C.G) Guided by Dr. Sanjay Kumar Dr. Vinod Kumar
Patle
2. Contents INTRODUCTION OF MATLAB INTRODUCTION OF PARALLEL
COMPUTING PARALLEL COMPUTING WITH MATLAB MATLAB FOR MULTI CORE
SYSTEM MATLAB FOR DISTRIBUTED COMPUTING SERVER PARALLEL COMANDS IN
MATLAB TEST THE EFFICINCY OF PARALLEL CODE CONCLUSION 3.
Introduction MATLAB is a high-level technical computing language
and interactive environment for algorithm development, data
visualization, data analysis, and numeric computation. Using the
MATLAB we can solve computing problems faster than with traditional
programming languages, such as C, C++, and Fortran. We can use
MATLAB in a wide range of applications, including signal and image
processing, communications, control design, test and measurement,
financial modeling and analysis ,computational biology and parallel
computing. 4. MATLAB Environment 5. Generally, computer code is
written in serial One task completed after another until the script
is finished with only one task completing at each time Because
computer only has single processing unit . What is Parallel
Computing? 6. What is Parallel Computing? (cont.) Parallel
Computing: Using multiple computer processing units (CPUs) to solve
a problem at the same time. computer with multiple processors or
networked computers(Distributed computing ) 7. PARALLEL MATLAB
Single Multi Core CPU Distributed Computing Server Number of core
on single machine as a worker to execute a task parallel like
OpenMP Client machine having there core which is take as workers in
network with central control of server . PARALLEL COMPUTING WITH
MATLAB MATLAB provide Parallel computing tool for Distributed
Computing Server as well as Single desktop . 8. MATLAB FOR MULTI
CORE SYSTEM MATLAB Provides workers (MATLAB computational engines)
to execute applications on a multi core system. We can write a
commands that will be executed in parallel or call an MATLAB script
file that will run in parallel Fig:- MATLAB workers for multi core
processor 9. MATLAB FOR DISTRIBUTED COMPUTING SERVER The
Distributed Computing Server controls parallel execution of MATLAB
on a cluster with tens or hundreds of cores With a cluster running
parallel MATLAB, we can submit an Matlab file from a client, to run
on the cluster or we can submit an Matlab file to be executed in
batch" Fig:- MATLAB Distributed Computing Server 10. PARALLEL
COMMANDS IN MATLAB findResource Code performaces commands tic &
toc , cputime,profile viewer etc. Matlabpool parfor (for loop)
pmode spmd (distributed computing for datasets) batch jobs (run job
in background) 11. 1. FIND RESOURCE This command give available
parallel computing resources Syntax out = findResource() out
=findResource('scheduler','configuration','ConfigurationName') 12.
Code Performance Commands Use MATLABs tic & toc functions tic
starts a timer toc tells you the number of seconds since the tic
function was called. 1. TIC & TOC Syntax tic ticID = tic toc
toc(ticID) elapsedTime = toc Example :- 13. 2. CPU TIME This
command returns the total CPU time (in seconds) used by MATLAB
application from the time it was started Syntax cputime Out=
cputime Example 14. 1. PROFILE VIEWER This command gives profile
records information about execution time, number of calls, parent
functions, child functions, code line hit count, and code line
execution time. Syntax: - profile on; profile off; profile resume;
profile clear; profile viewer; Example:- 15. PROFILE VIEWER WINDOW
16. MATLABPOOL This command open or close pool of MATLAB sessions
for parallel computation .It starts a worker pool using the default
parallel configuration, with the pool size specified by
configuration. Syntax matlabpool matlabpool open matlabpool open
poolsize matlabpool open configname matlabpool close matlabpool
close force matlabpool close force configname Example:- 17. Request
for too many workers, get an error only request 2 workers on this
machine! 18. Matlabpool Close Use matlabpool close to end parallel
session Options matlabpool close force deletes all pool jobs for
current user in the cluster specified by default profile (including
running jobs) matlabpool close force deletes all pool jobs run in
the specified profile 19. Parallel for Loops (parfor) parfor loops
can execute for loop like code in parallel to significantly improve
performance Must consist of code broken into discrete parts that
can be solved simultaneously (i.e. it cant be serial) Matlab
workers evaluate iterations in no particular order and
independently of each other. Syntax parfor loopvar =
initval:endval; statements; end parfor (loopvar = initval:endval,
M); statements; end 20. Parfor example work in parallel loop
increments are not dependent on each other: Makes the loop run in
parallel 21. Test the efficiency of parallel code Observed speedup
of a code which has been parallelized defined as :- Execution time
of Serial code Execution time of parallel code Speedup = One of the
simplest and widely used speedup formula for parallel programs
performances. For the testing the efficiency of parfor loop we
considered prime number finding program .which limits is increasing
ordered like 10 ,100,1000,10000 and 100000 iteration 22. Simple for
loop program :- function [ total ] = simpleprime( n ) %SIMPLEPRIME
Summary of this function goes here % Detailed explanation goes here
total = 0 ; tic for i = 2 : n prime = 1 ; for j = 2 : i-1 if( mod (
i , j ) == 0 ) prime = 0 ; end end total = total + prime ; end toc
return end n=10; while ( n
