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Accelerating BER Simulations Using the Parallel Computing Toolbox

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This example uses Parallel Computing Toolbox™ to accelerate a simple, QPSK bit error rate (BER) simulation. The system consists of a QPSK modulator, a QPSK demodulator, an AWGN channel, and a bit error rate counter.

Set the simulation parameters.

EbNoVec = 5:8;     % Eb/No values in dB
totalErrors = 200; % Number of bit errors needed for each Eb/No value
totalBits = 1e7;   % Total number of bits transmitted for each Eb/No value

Allocate memory to the arrays used to store the data generated by the function, helper_qpsk_sim_with_awgn.

[numErrors, numBits] = deal(zeros(length(EbNoVec),1));

Run the simulation and determine the execution time. Only one processor will be used to determine baseline performance. Accordingly, observe that the normal for-loop is employed.

tic
for idx = 1:length(EbNoVec)
    errorStats = helper_qpsk_sim_with_awgn(EbNoVec,idx, ...
        totalErrors,totalBits);
    numErrors(idx) = errorStats(idx,2);
    numBits(idx) = errorStats(idx,3);
end
simBaselineTime = toc;

Calculate the BER.

ber1 = numErrors ./ numBits;

Rerun the simulation for the case in which Parallel Computing Toolbox is available. Create a pool of workers.

pool = gcp;
assert(~isempty(pool), ['Cannot create parallel pool. '...
  'Try creating the pool manually using ''parpool'' command.'])

Determine the number of available workers from the NumWorkers property of pool. The simulation runs the range of $E_{b}/N_{0}$ values over each worker rather than assigning a single $E_{b}/N_{0}$ point to each worker as the former method provides the biggest performance improvement.

numWorkers = pool.NumWorkers;

Determine the length of EbNoVec for use in the nested parfor loop. For proper variable classification, the range of a for-loop nested in a parfor must be defined by constant numbers or variables.

lenEbNoVec = length(EbNoVec);

Allocate memory to the arrays used to store the data generated by the function, helper_qpsk_sim_with_awgn.

[numErrors,numBits] = deal(zeros(length(EbNoVec),numWorkers));

Run the simulation and determine the execution time.

tic
parfor n = 1:numWorkers
    for idx = 1:lenEbNoVec
        errorStats = helper_qpsk_sim_with_awgn(EbNoVec,idx, ...
            totalErrors/numWorkers,totalBits/numWorkers);
        numErrors(idx,n) = errorStats(idx,2);
        numBits(idx,n) = errorStats(idx,3);
    end
end
simParallelTime = toc;

Calculate the BER. In this case, the results from multiple processors must be combined to generate the aggregate BER.

ber2 = sum(numErrors,2) ./ sum(numBits,2);

Compare the BER values to verify that the same results are obtained independent of the number of workers.

semilogy(EbNoVec',ber1,'-*',EbNoVec',ber2,'-^')
legend('Single Processor','Multiple Processors','location','best')
xlabel('Eb/No (dB)')
ylabel('BER')
grid

You can see that the BER curves are essentially the same with any variance being due to differing random number seeds.

Compare the execution times for each method.

fprintf(['\nSimulation time = %4.1f sec for one worker\n', ...
    'Simulation time = %4.1f sec for multiple workers\n'], ...
    simBaselineTime,simParallelTime)
fprintf('Number of processors for parfor = %d\n', numWorkers)

Simulation time = 24.6 sec for one worker
Simulation time =  6.1 sec for multiple workers
Number of processors for parfor = 6

See Also

| (Parallel Computing Toolbox)

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