#MATLAB RESHAPE HOW TO#
#MATLAB RESHAPE SERIAL#
#MATLAB RESHAPE CODE#
#MATLAB RESHAPE FREE#

#MATLAB RESHAPE FREE#

% Apply the free space path loss formula to the antenna power % Calculate the distance from map position ii to antenna jj % Get the power received from every mast at this grid point NN = 10 % This version is too slow to process more than a few points

SignalPowerDecibels = gpuArray.zeros(M, 1) To add some additional real-world complexity to the calculation, antennae that are pointing away from the location cannot be included (the signal strength is effectively zero).

#MATLAB RESHAPE CODE#

Since modern cell phones are in communication with multiple transmitters at once, the code averages the three strongest signals. It loops over every grid point and every antenna, computing one value per grid point. The following code calculates the peak signal strength at each grid point on the map by measuring the losses between the grid point and the transmitters.

#MATLAB RESHAPE SERIAL#

This code runs a lot faster, especially on the GPU which was crippled by very low utilization in the serial code (we were asking it to do too little work at a time). AntennaDirection = Elevation * AntennaDirection The rules of matrix multiplication let me do this without a loop. Note, however, that thereвЂ™s no dependency between one rotation and the next. NewAntennaDirection(:,ii) = Elevation * AntennaDirection(:,ii) NewAntennaDirection = gpuArray.zeros(size(AntennaDirection)) % Allocate a new array of directions and loop through to compute them LetвЂ™s say I need to rotate all the antennae downwards by 10 degrees, as the following code shows. The simplest approach to vectorization in MATLAB is to take advantage of matrix algebra in your mathematical operations. Masts = gpuArray() ДЄntennaDirection = gpuArray(AntennaDirection) Vectorization basicsД¬omputational hot spots in code generally appear as loops or repeated segments of code, where the repeated operations are naturally parallel in other words, they donвЂ™t depend on each other. % positions into a single 3-column matrix containing all % Put the map data on the GPU and concatenate the map MastIndex = lon(1,M) % 1 x M array of mast indices MapIndex = lon(1,N)' % N x 1 array of map indices Pan = [cosd(turnAngle) -sind(turnAngle) Zero % Set up some random rotation matrices, stacked along the 3rd % horizontal vectors representing the direction the antennae are facing.ДЄntennaDirection = % Finally, give each antenna a random orientation. Power() = 0 % A small number are out of order Power(1:4:M) = 400 % A few transmitters are more powerful Power = 100 * ones(1, M) % Most transmitters use 100 W of power H = 20 % All masts are 20 meters in heightДЇrequency = 800e6 % 800 MHz transmitters % Antenna masts - index into map spacing every 20 gridpoints

AntennaDirection: A 3 x M array of vectors representing the orientation of each antenna.

masts: An M x 3 array of antenna positions, at height H.

map: An N x 3 height field in a 10km x 10km grid ( N = 10,000).

On the GPU, in the following listing we define a number of variables including: Figure 1: 3D plot of the map data and antenna positions We imagine an idealized scenario with M = 25 cellphone masts, each H = 20 meters in height, evenly spaced on an undulating 10km x 10km terrain.В Figure 1 shows what the map looks like. A cellular phone network wants to map its coverage to help plan for new antenna installations. Throughout this post, I will use an example to help illustrate the techniques. If these techniques do not provide the performance or flexibility you were after, you can still write custom CUDA code in C or C++ that you can run from MATLAB, as discussed in our earlier Parallel Forall posts on MATLAB CUDA KernelsВ and MEX functions.ДЄll of the features described here are available out of the box with MATLAB and Parallel Computing Toolboxв„ў. The most advanced function, arrayfun, allows you to write your own custom kernels in the MATLAB language. Then I present a family of function wrappers- bsxfun, pagefun, and arrayfun-that take advantage of GPU hardware, yet require no specialist parallel programming skills. This technique, known as vectorization, benefits all your code whether or not it uses the GPU.

#MATLAB RESHAPE HOW TO#

First I explain how to write MATLAB code which is inherently parallelizable. In this post, I will discuss techniques you can use to maximize the performance of your GPU-accelerated MATLABВ® code.

Matlab reshape