This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Parfeval Blackjack

This example uses Parallel Computing Toolbox™ to play the card game of blackjack, also known as 21. We simulate a number of players that are independently playing thousands of hands at a time, and display payoff statistics. This example runs the simulations asynchronously on a parallel pool of workers, using parfeval. In this way, we can update a display of the results as they become available.

Related examples:

You can find the code shown in this example in the function:

function paralleldemo_blackjack_parfeval

Analyze the sequential problem

Because the blackjack players are independent of one another, we can simulate them in parallel. We do this by dividing the problem up into a number of function evaluations. We run a maximum of numPlayers simulations, where each player plays numHands hands of blackjack. We plot the results as soon as they become available, and we terminate the simulation if the elapsed time exceeds maxSimulationTime seconds, or if the user cancels execution.

numPlayers        = 100;
numHands          = 5000;
maxSimulationTime = 20;

Divide the work into individual function evaluations

We call the parfeval function to request evaluation of the simulation on the parallel pool workers. The parallel pool will be created automatically if necessary. The parfeval function returns a parallel.Future object, which we use to access results when they become available. You can view the code for pctdemo_task_blackjack for full details.

for idx = numPlayers:-1:1
    futures(idx) = parfeval(@pctdemo_task_blackjack, 1, numHands, 1);
% Create an onCleanup to ensure we do not leave any futures running when we exit
% this example.
cancelFutures = onCleanup(@() cancel(futures));

Set up for collecting results and monitoring progress

The parallel pool workers immediately start running pctdemo_task_blackjack, and we can collect and display results as soon as they are available by using the fetchNext method. We use resultsSoFar to accumulate results. We update the array completed to indicate which elements of futures have completed, and increment the counter numCompleted. We supply the optional argument timeout to the fetchNext method so that it returns quickly if no new results are available.

resultsSoFar = zeros(numHands, numPlayers); % Allocate space for all results
completed    = false(1, numPlayers);        % Has a given future completed yet
timeout      = 2;                           % fetchNext timeout in seconds
numCompleted = 0;                           % How many simulations have completed
fig          = pctdemo_setup_blackjack(1);  % Create a figure to display results

% Build a waitbar with a cancel button, using appdata to track
% whether the cancel button has been pressed.
hWaitBar = waitbar(0, 'Blackjack progress', 'CreateCancelBtn', ...
                   @(src, event) setappdata(gcbf(), 'Cancelled', true));
setappdata(hWaitBar, 'Cancelled', false);

Collect and display results as they become available

We collect and display results by calling fetchNext in a loop until we have seen numPlayers results. When fetchNext returns new results, we assign the results into resultsSoFar, update the completed array and the numCompleted counter, and update the plot. We abort the loop early if the user presses the cancel button on the waitbar, or the maxSimulationTime expires.

startTime = clock();
while numCompleted < numPlayers

    % fetchNext blocks execution until one element of futures has completed.  It
    % then returns the index into futures of the element that has now completed,
    % and the results from execution.
    [completedIdx, resultThisTime] = fetchNext(futures, timeout);

    % If fetchNext timed out returning an empty completedIdx, do not attempt to
    % process results.
    if ~isempty(completedIdx)
        numCompleted = numCompleted + 1;
        % Update list of completed futures.
        completed(completedIdx) = true;
        % Fill out portion of results.
        resultsSoFar(:, completedIdx) = resultThisTime;
        % Update plot.
        pctdemo_plot_blackjack(fig, resultsSoFar(:, completed), false);

    % Check to see if we have run out of time.
    timeElapsed = etime(clock(), startTime);
    if timeElapsed > maxSimulationTime
        fprintf('Simulation terminating: maxSimulationTime exceeded.\n');

    % Check to see if the cancel button was pressed.
    if getappdata(hWaitBar, 'Cancelled')
        fprintf('Simulation cancelled.\n');

    % Update the waitbar.
    fractionTimeElapsed      = timeElapsed / maxSimulationTime;
    fractionPlayersCompleted = numCompleted / numPlayers;
    fractionComplete         = max(fractionTimeElapsed, fractionPlayersCompleted);
    waitbar(fractionComplete, hWaitBar);
fprintf('Number of simulations completed: %d\n', numCompleted);

% Now the simulation is complete, we can cancel the futures and delete
% the waitbar.
Simulation terminating: maxSimulationTime exceeded.
Number of simulations completed: 74

Was this topic helpful?