Speed up Matlab

Xin Sheng Zhou

Matlab

MATrix LABoratory Started in the late 1970s

Matrix manipulation

Plotting of data

Implementation of algorithms

Creation of user interface

Interfacing with other languages, such as C and Fortran

Slower, but reducing programming effort

Java

Java based Appeared in 1995

Run on Java Virtual Machine

Compile once, run everywhere

Inherited slower than C/C++

Currently (February 2012), microbenchmarks show Java 7 is approximately 1.5 times slower than C

Code 1

Preallocating arrays

Preallocating arrays

Looking for larger memory

Moving data

Preallocating arrays

Looking for larger memory

Moving data

Preallocating arrays

Preallocating a non-double matrix

A = int8(zeros(100));

A = zeros(100, 'int8');

Changing a variable’s data type or

dimension

Another case: assigning real and complex

numbers

Using appropriate logical operators

Large if…else… block, consider

switch…case…

Code 2

Vectorizing loops

Profiling

Measure where a program spends time

Case study

Low density parity check decoder

Block decoding

Block length: 106

Generate parity check matrix

Decoding

Resource used for block length

10^6 and 10^7

LDPC Code

H*cT=0

For code ½, block length 106

H: Matrix (0.5*106,106)

C: Row vector, size 106

Encoding: c=s*G s: row vector, size 0.5*106

G: matrix, (0.5*106,106)

Get G from H is not easy

Instead, generate parity check bits p=A-1Bs H=[A B], c=[p s]

Matlab’s matrix multiplication and inversion cannot be used since it is based on double value and encoding is based on GF(2)

Solution: Custom matrix inversion and multiplication on GF(2)

Inversion is slow, and A-1 is required to be saved

When block length > 105, saving is also slow

Since H is sparse, sparse matrix format should be used. However, A-1 is not sparse

Other ways…

Parallel computing

Multi-threaded

GPU

Multi-machine

MEX file

Matlab Compiler

Summary

Preallocating arrays

Vectorizing loops

Profiling

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