Avoiding race conditions when using parfor in MATLAB - matlab

Question

I'm looping in parallel and changing a variable if a condition is met. Super idiomatic code that I'm sure everyone has written a hundred times:
trials = 100;
greatest_so_far = 0;
best_result = 0;
for trial_i = 1:trials
[amount, result] = do_work();
if amount > greatest_so_far
greatest_so_far = amount;
best_result = result;
end
end
If I wanted to replace for by parfor, how can I ensure that there aren't race conditions when checking whether we should replace greatest_so_far? Is there a way to lock this variable outside of the check? Perhaps like:
trials = 100;
greatest_so_far = 0;
best_result = 0;
parfor trial_i = 1:trials
[amount, result] = do_work();
somehow_lock(greatest_so_far);
if amount > greatest_so_far
greatest_so_far = amount;
best_result = result;
end
somehow_unlock(greatest_so_far);
end

Answer 1

Skewed answer. It does not exactly solve your problem, but it might help you avoiding it.
If you can afford the memory to store the outputs of your do_work() in some vectors, then you could simply run your parfor on this function only, store the result, then do your scoring at the end (outside of the loop):
amount = zeros( trials , 1 ) ;
result = zeros( trials , 1 ) ;
parfor trial_i = 1:trials
[amount(i), result(i)] = do_work();
end
[ greatest_of_all , greatest_index ] = max(amount) ;
best_result = result(greatest_index) ;
Edit/comment : (wanted to put that in comment of your question but it was too long, sorry).
I am familiar with .net and understand completely your lock/unlock request. I myself tried many attempts to implement a kind of progress indicator for very long parfor loop ... to no avail.
If I understand Matlab classification of variable correctly, the mere fact that you assign greatest_so_far (in greatest_so_far=amount) make Matlab treat it as a temporary variable, which will be cleared and reinitialized at the beginning of every loop iteration (hence unusable for your purpose).
So an easy locked variable may not be a concept we can implement simply at the moment. Some convoluted class event or file writing/checking may do the trick but I am afraid the timing would suffer greatly. If each iteration takes a long time to execute, the overhead might be worth it, but if you use parfoor to accelerate a high number of short execution iterations, then the convoluted solutions would slow you down more than help ...
You can have a look at this stack exchange question, you may find something of interest for your case: Semaphores and locks in MATLAB

Answer 2

The solution from Hoki is the right way to solve the problem as stated. However, as you asked about race conditions and preventing them when loop iterations depend on each other you might want to investigate spmd and the various lab* functions.

Answer 3

You need to use SPMD to do this - SPMD allows communication between the workers. Something like this:
bestResult = -Inf;
bestIndex = NaN;
N = 97;
spmd
% we need to round up the loop range to ensure that each
% worker executes the same number of iterations
loopRange = numlabs * ceil(N / numlabs);
for idx = 1:numlabs:loopRange
if idx <= N
local_result = rand(); % obviously replace this with your actual function
else
local_result = -Inf;
end
% Work out which index has the best result - use a really simple approach
% by concatenating all the results this time from each worker
% allResultsThisTime will be 2-by-numlabs where the first row is all the
% the results this time, and the second row is all the values of idx from this time
allResultsThisTime = gcat([local_result; idx]);
% The best result this time - consider the first row
[bestResultThisTime, labOfBestResult] = max(allResultsThisTime(1, :));
if bestResultThisTime > bestResult
bestResult = bestResultThisTime;
bestIndex = allResultsThisTime(2, labOfBestResult);
end
end
end
disp(bestResult{1})
disp(bestIndex{1})