Простое сравнительное тестирование PARFOR Используя блэк джек

Этот пример тестирует в сравнении с эталоном parfor построение путем повторного проигрывания карточной игры блэк джека, также известного 21. Мы используем parfor проигрывать карточную игру многократно параллельно, варьируясь количество рабочих MATLAB®, но всегда с помощью того же числа игроков и рук.

Связанные примеры:

Параллельная версия

Основной параллельный алгоритм использует parfor создайте, чтобы выполнить независимые передачи через цикл. Это - часть языка MATLAB®, но ведет себя по существу как регулярный for- цикл, если у вас нет доступа к продукту Parallel Computing Toolbox™. Таким образом наш начальный шаг должен преобразовать цикл формы

for i = 1:numPlayers
   S(:, i) = playBlackjack();
end

в эквивалентный parfor цикл:

parfor i = 1:numPlayers
   S(:, i) = playBlackjack();
end

Мы изменяем это немного путем определения дополнительного аргумента к parfor, то, чтобы давать команду ему ограничить n количество рабочих это использует в расчетах. Фактический код следующие:

dbtype pctdemo_aux_parforbench

1     function S = pctdemo_aux_parforbench(numHands, numPlayers, n)
2     %PCTDEMO_AUX_PARFORBENCH Use parfor to play blackjack.
3     %   S = pctdemo_aux_parforbench(numHands, numPlayers, n) plays 
4     %   numHands hands of blackjack numPlayers times, and uses no 
5     %   more than n MATLAB(R) workers for the computations.
6     
7     %   Copyright 2007-2009 The MathWorks, Inc.
8     
9     S = zeros(numHands, numPlayers);
10    parfor (i = 1:numPlayers, n)
11        S(:, i) = pctdemo_task_blackjack(numHands, 1);
12    end

Проверяйте состояние параллельного пула

Мы будем использовать параллельный пул, чтобы позволить тело parfor цикл, чтобы запуститься параллельно, таким образом, мы запускаем путем проверки, открыт ли пул. Мы затем запустим сравнительный тест, использующий куда угодно между 2 и poolSize рабочие от этого пула.

p = gcp;
if isempty(p)
    error('pctexample:backslashbench:poolClosed', ...
        ['This example requires a parallel pool. ' ...
         'Manually start a pool using the parpool command or set ' ...
         'your parallel preferences to automatically start a pool.']);
end
poolSize = p.NumWorkers;

Запустите сравнительный тест: слабое масштабирование

Мы время выполнение наших вычислений сравнительного теста с помощью 2 к poolSize рабочие. Мы используем слабое масштабирование, то есть, мы увеличиваем проблемный размер с количеством рабочих.

numHands = 2000;
numPlayers = 6;
fprintf('Simulating each player playing %d hands.\n', numHands);
t1 = zeros(1, poolSize);
for n = 2:poolSize
    tic;
        pctdemo_aux_parforbench(numHands, n*numPlayers, n);
    t1(n) = toc;
    fprintf('%d workers simulated %d players in %3.2f seconds.\n', ...
            n, n*numPlayers, t1(n));
end

Simulating each player playing 2000 hands.
2 workers simulated 12 players in 10.81 seconds.
3 workers simulated 18 players in 10.67 seconds.
4 workers simulated 24 players in 10.57 seconds.
5 workers simulated 30 players in 10.57 seconds.
6 workers simulated 36 players in 10.71 seconds.
7 workers simulated 42 players in 10.63 seconds.
8 workers simulated 48 players in 10.87 seconds.
9 workers simulated 54 players in 10.54 seconds.
10 workers simulated 60 players in 10.73 seconds.
11 workers simulated 66 players in 10.58 seconds.
12 workers simulated 72 players in 10.68 seconds.
13 workers simulated 78 players in 10.56 seconds.
14 workers simulated 84 players in 10.89 seconds.
15 workers simulated 90 players in 10.62 seconds.
16 workers simulated 96 players in 10.63 seconds.
17 workers simulated 102 players in 10.70 seconds.
18 workers simulated 108 players in 10.70 seconds.
19 workers simulated 114 players in 10.79 seconds.
20 workers simulated 120 players in 10.72 seconds.
21 workers simulated 126 players in 10.74 seconds.
22 workers simulated 132 players in 10.75 seconds.
23 workers simulated 138 players in 10.74 seconds.
24 workers simulated 144 players in 10.72 seconds.
25 workers simulated 150 players in 10.74 seconds.
26 workers simulated 156 players in 10.76 seconds.
27 workers simulated 162 players in 10.74 seconds.
28 workers simulated 168 players in 10.72 seconds.
29 workers simulated 174 players in 10.76 seconds.
30 workers simulated 180 players in 10.69 seconds.
31 workers simulated 186 players in 10.76 seconds.
32 workers simulated 192 players in 10.76 seconds.
33 workers simulated 198 players in 10.79 seconds.
34 workers simulated 204 players in 10.74 seconds.
35 workers simulated 210 players in 12.12 seconds.
36 workers simulated 216 players in 12.19 seconds.
37 workers simulated 222 players in 12.19 seconds.
38 workers simulated 228 players in 12.14 seconds.
39 workers simulated 234 players in 12.15 seconds.
40 workers simulated 240 players in 12.18 seconds.
41 workers simulated 246 players in 12.18 seconds.
42 workers simulated 252 players in 12.14 seconds.
43 workers simulated 258 players in 12.24 seconds.
44 workers simulated 264 players in 12.25 seconds.
45 workers simulated 270 players in 12.23 seconds.
46 workers simulated 276 players in 12.23 seconds.
47 workers simulated 282 players in 12.55 seconds.
48 workers simulated 288 players in 12.52 seconds.
49 workers simulated 294 players in 13.24 seconds.
50 workers simulated 300 players in 13.28 seconds.
51 workers simulated 306 players in 13.36 seconds.
52 workers simulated 312 players in 13.53 seconds.
53 workers simulated 318 players in 13.98 seconds.
54 workers simulated 324 players in 13.90 seconds.
55 workers simulated 330 players in 14.29 seconds.
56 workers simulated 336 players in 14.23 seconds.
57 workers simulated 342 players in 14.25 seconds.
58 workers simulated 348 players in 14.32 seconds.
59 workers simulated 354 players in 14.26 seconds.
60 workers simulated 360 players in 14.34 seconds.
61 workers simulated 366 players in 15.60 seconds.
62 workers simulated 372 players in 15.75 seconds.
63 workers simulated 378 players in 15.79 seconds.
64 workers simulated 384 players in 15.76 seconds.

Мы сравниваем это с выполнением с помощью регулярного for- цикл в MATLAB®.

tic;
    S = zeros(numHands, numPlayers);
    for i = 1:numPlayers
        S(:, i) = pctdemo_task_blackjack(numHands, 1);
    end
t1(1) = toc;
fprintf('Ran in %3.2f seconds using a sequential for-loop.\n', t1(1));

Ran in 10.70 seconds using a sequential for-loop.

Постройте ускорение

Мы сравниваем ускорение с помощью parfor с различными количествами рабочих к совершенно линейной кривой ускорения. Ускорение достигается при помощи parfor зависит от проблемного размера, а также используемого оборудования и сетевой инфраструктуры.

speedup = (1:poolSize).*t1(1)./t1;
fig = pctdemo_setup_blackjack(1.0);
fig.Visible = 'on';
ax = axes('parent', fig);
x = plot(ax, 1:poolSize, 1:poolSize, '--', ...
    1:poolSize, speedup, 's', 'MarkerFaceColor', 'b');
t = ax.XTick;
t(t ~= round(t)) = []; % Remove all non-integer x-axis ticks.
ax.XTick = t;
legend(x, 'Linear Speedup', 'Measured Speedup', 'Location', 'NorthWest');
xlabel(ax, 'Number of MATLAB workers participating in computations');
ylabel(ax, 'Speedup');

Измерьте распределение ускорения

Чтобы получить надежные числа сравнительного теста, мы должны запустить сравнительный тест многократно. Мы поэтому запускаем сравнительный тест многократно для poolSize рабочие, чтобы позволить нам смотреть на распространение ускорения.

numIter = 100;
t2 = zeros(1, numIter);
for i = 1:numIter
    tic;
        pctdemo_aux_parforbench(numHands, poolSize*numPlayers, poolSize);
    t2(i) = toc;
    if mod(i,20) == 0
        fprintf('Benchmark has run %d out of %d times.\n',i,numIter);
    end
end

Benchmark has run 20 out of 100 times.
Benchmark has run 40 out of 100 times.
Benchmark has run 60 out of 100 times.
Benchmark has run 80 out of 100 times.
Benchmark has run 100 out of 100 times.

Постройте распределение ускорения

Мы внимательно изучаем ускорение нашей простой параллельной программы при использовании максимального количества рабочих. Гистограмма ускорения позволяет нам различать выбросы и среднее ускорение.

speedup = t1(1)./t2*poolSize;
clf(fig);
ax = axes('parent', fig);
hist(speedup, 5);
a = axis(ax);
a(4) = 5*ceil(a(4)/5); % Round y-axis to nearest multiple of 5.
axis(ax, a)
xlabel(ax, 'Speedup');
ylabel(ax, 'Frequency');
title(ax, sprintf('Speedup of parfor with %d workers', poolSize));
m = median(speedup);
fprintf(['Median speedup is %3.2f, which corresponds to '...
    'efficiency of %3.2f.\n'], m, m/poolSize);

Median speedup is 43.37, which corresponds to efficiency of 0.68.

Документация