• 目次

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1. MathWorksアカウント画面へ

   

2. MATLAB（Individual）の右の下矢印を押してダウンロード画面へ
   ※この際に画面左のリリースを選択からR2022aを選択します。

   

3. {Windows, Mac, Linux}用ダウンロードを押してMATLAB R2022aのダウンロードを開始します。

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Parallel Serverを使用するには、インストール時の製品の選択ではMATLABの他にParallel Computing Toolboxを選択してください。

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準備3-

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クラスター構成

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例

6. クラスタープロファイルの切り替え（localとa9 Server）

localとa9 Serverの切り替えは、それぞれのクラスタープロファイルについて既定の設定を切り替えて行います。

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7. 並列処理ありとなしの切り替え

並列処理ありとなしの切り替えは、コマンドにおける'UseParallel',trueを'UseParallel',falseに書き換える。

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tic % Start stopwatch timer
% Input image
input_img = "input_img.jpg"; % Add image path
% Initialize Edge detection function
fun = @(block_struct) edge(block_struct.data,"canny");
% Covert source image from RGB to GRAY
input_image= rgb2gray(imread(input_img));
% Perform Parallel Block Process
result = blockproc(input_image,[25 25],fun, ...
   'UseParallel',false);
toc % Terminate stopwatch timer 
% Show ouput image
imshow(result

並列処理の例

例1：大きな画像に対するブロック処理

input_img.pngをC:\Users\ユーザー名\Documents\MATLABに移す。

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例2：グローバルミニマムの探索

1. 並列処理ありの場合

   Code Block

   tic % Start stopwatch timer % Consider a function with several local minima. fun = @(x) x.^2 + 4*sin(5*x); fplot(fun,[-10,10]) rng default % For reproducibility opts = optimoptions(@fmincon,'Algorithm','sqp'); problem = createOptimProblem('fmincon','objective',... fun,'x0',3,'lb',-5,'ub',5,'options',opts); ms = MultiStart('UseParallel', true); %To search for the global minimum, run MultiStart on 2000 instances of the problem using the fmincon 'sqp' algorithm. [x,f] = run(ms,problem,2000) toc % Terminate stopwatch timer

   

2. 並列処理なしの場合

   Code Block

   tic % Start stopwatch timer % Consider a function with several local minima. fun = @(x) x.^2 + 4*sin(5*x); fplot(fun,[-10,10]) rng default % For reproducibility opts = optimoptions(@fmincon,'Algorithm','sqp'); problem = createOptimProblem('fmincon','objective',... fun,'x0',3,'lb',-5,'ub',5,'options',opts); ms = MultiStart('UseParallel', false); %To search for the global minimum, run MultiStart on 2000 instances of the problem using the fmincon 'sqp' algorithm. [x,f] = run(ms,problem,2000) toc % Terminate stopwatch timer

3. 処理時間の比較（単位：秒）

例3：SVM分類器による最適化

1. 並列処理ありの場合

   Code Block

   tic % Start stopwatch timer load ionosphere % Load the ionosphere data set. rng default % Find hyperparameters that minimize five-fold cross-validation loss by using automatic hyperparameter optimization. For reproducibility, set the random seed and use the 'expected-improvement-plus' acquisition function. Mdl = fitcsvm(X,Y,'OptimizeHyperparameters','auto', ... 'HyperparameterOptimizationOptions',struct('UseParallel',true)) toc % Terminate stopwatch timer

   

2. 並列処理なしの場合

   Code Block

   tic % Start stopwatch timer load ionosphere % Load the ionosphere data set. rng default % Find hyperparameters that minimize five-fold cross-validation loss by using automatic hyperparameter optimization. For reproducibility, set the random seed and use the 'expected-improvement-plus' acquisition function. Mdl = fitcsvm(X,Y,'OptimizeHyperparameters','auto', ... 'HyperparameterOptimizationOptions',struct('UseParallel',false)) toc % Terminate stopwatch timer

3. 処理時間の比較（単位：秒）

例4：並列処理によるデータのクラスタリング

1. 並列処理ありの場合

   Code Block

   Mu = bsxfun(@times,ones(20,300),(1:20)'); % Gaussian mixture mean rn300 = randn(300,300); Sigma = rn300'*rn300; % Symmetric and positive-definite covariance Mdl = gmdistribution(Mu,Sigma); % Define the Gaussian mixture distribution rng(1); % For reproducibility X = random(Mdl,10000); % Specify the options for parallel computing. stream = RandStream('mlfg6331_64'); % Random number stream options = statset('UseParallel',1,'UseSubstreams',1,... 'Streams',stream); % Cluster the data using k-means clustering. Specify that there are k = 200 clusters in the data and increase the number of iterations. Typically, the objective function contains local minima. Specify 10 replicates to help find a lower, local minimum. tic; % Start stopwatch timer [idx,C,sumd,D] = kmeans(X,200,'Options',options,'MaxIter',10000,... 'Display','final','Replicates',10); toc % Terminate stopwatch timer

   

2. 並列処理なしの場合

   Code Block

   Mu = bsxfun(@times,ones(20,300),(1:20)'); % Gaussian mixture mean rn300 = randn(300,300); Sigma = rn300'*rn300; % Symmetric and positive-definite covariance Mdl = gmdistribution(Mu,Sigma); % Define the Gaussian mixture distribution rng(1); % For reproducibility X = random(Mdl,10000); % Specify the options for parallel computing. stream = RandStream('mlfg6331_64'); % Random number stream options = statset('UseParallel',false,'UseSubstreams',1,... 'Streams',stream); % Cluster the data using k-means clustering. Specify that there are k = 200 clusters in the data and increase the number of iterations. Typically, the objective function contains local minima. Specify 10 replicates to help find a lower, local minimum. tic; % Start stopwatch timer [idx,C,sumd,D] = kmeans(X,200,'Options',options,'MaxIter',10000,... 'Display','final','Replicates',10); toc % Terminate stopwatch timer

3. 処理時間の比較（単位：秒）

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