Third-Party Prerequisites

Required

This example generates CUDA MEX and has the following third-party requirements.

Optional

For non-MEX builds such as static, dynamic libraries or executables, this example has the following additional requirements.

Verify GPU Environment

To verify that the compilers and libraries necessary for running this example are set up correctly, use thecoder.checkGpuInstallfunction.

envCfg = coder.gpuEnvConfig('host'); envCfg.BasicCodegen = 1; envCfg.Quiet = 1; coder.checkGpuInstall(envCfg);

Generate a Random Initial Population

Being that the game is zero-player, the evolution of the game is determined by its initial state. For this example, an initial population of cells is created on a two-dimensional grid with approximately 25% of the locations being alive.

gridSize = 500; numGenerations = 100; initialGrid = (rand(gridSize,gridSize) > .75);% Draw the initial gridimagesc(initialGrid); colormap([1 1 1;0 0.5 0]); title('Initial Grid');

Play the Game of Life

Thegameoflife_orig.mfunction is a fully vectorized implementation of "Game of Life". The function updates all cells on the grid in one pass per their generation.

typegameoflife_orig

% % MATLAB栅格矢量化的实现功能= gameoflife_orig(initialGrid) % Copyright 2016-2019 The MathWorks, Inc. numGenerations = 100; grid = initialGrid; [gridSize,~] = size(initialGrid); % Loop through each generation updating the grid and displaying it. for generation = 1:numGenerations grid = updateGrid(grid, gridSize); imagesc(grid); colormap([1 1 1;0 0.5 0]); title(['Grid at Iteration ',num2str(generation)]); drawnow; end function X = updateGrid(X, N) % Index vectors increase or decrease the centered index by one % thereby accessing neighbors to the left,right,up, and down. p = [1 1:N-1]; q = [2:N N]; % Count how many of the eight neighbors are alive. neighbors = X(:,p) + X(:,q) + X(p,:) + X(q,:) + ... X(p,p) + X(q,q) + X(p,q) + X(q,p); % A live cell with two live neighbors, or any cell with % three live neighbors, is alive at the next step. X = (X & (neighbors == 2)) | (neighbors == 3); end end

Play the game by calling thegameoflife_origfunction with an initial population. The game iterates through 100 generations and displays the population at each generation.

gameoflife_orig(initialGrid);

Convert the Game of Life for GPU Code Generation

Looking at the calculations in theupdateGridfunction, it is apparent that the same operations are applied at each grid location independently. However, each cell must know about its eight neighbors. The modifiedgameoflife_stencil.mfunction uses thestencilfunpragma to compute a 3-by-3 region around each cell. The GPU Coder™ implementation of the stencil kernel computes one element of the grid in each thread and uses shared memory to improve memory bandwidth and data locality.

typegameoflife_stencil

function grid = gameoflife_stencil(initialGrid) %#codegen % Copyright 2016-2019 The MathWorks, Inc. numGenerations = 100; grid = initialGrid; % Loop through each generation updating the grid. for generation = 1:numGenerations grid = stencilfun(@updateElem, grid, [3,3], Shape='same'); end end function X = updateElem(window) neighbors = window(1,1) + window(1,2) + window(1,3) ... + window(2,1) + window(2,3) ... + window(3,1) + window(3,2) + window(3,3); X = (window(2,2) & (neighbors == 2)) | (neighbors == 3); end

Generate CUDA MEX for the Function

To generate CUDA MEX for thegameoflife_stencilfunction, create a GPU code configuration object, and then use thecodegencommand.

cfg = coder.gpuConfig('mex'); codegen-configcfg-args{initialGrid}gameoflife_stencil

Code generation successful.

Run the MEX Function

Run the generatedgameoflife_stencil_mexwith the random initial population.

gridGPU = gameoflife_stencil_mex(initialGrid);% Draw the grid after 100 generationsimagesc(gridGPU); colormap([1 1 1;0 0.5 0]); title('Final Grid - CUDA MEX');