To utilize parallel computing in MATLAB using parfor

by Posted on

To utilize parallel computing in MATLAB using parfor, follow this structured guide:

1. Prerequisites

  • Parallel Computing Toolbox: Ensure this toolbox is installed.
  • Start Parallel Pool: Use parpool to initiate workers. MATLAB may start a default pool automatically with parfor if none exists.

2. parfor Basics

  • Syntax: Replace for with parfor in loop constructs.
    matlab
    Copy
    parfor i = 1:n
    % Parallel computations
end
  • Independent Iterations: Loop iterations must be independent (no data dependencies between iterations).

3. Variable Classification

  • Sliced Variables: Arrays accessed by index i (e.g., A(i) = ...).
  • Broadcast Variables: Read-only variables used inside the loop (e.g., constants).
  • Reduction Variables: Accumulate values across iterations (e.g., sums: x = x + a(i)).
  • Temporary Variables: Created and used within a single iteration.

4. Key Considerations

  • Avoid Data Dependencies: Ensure no iteration depends on another.
  • Reduction Operations: Use for associative/commutative operations (e.g., sum, product).
  • Unsupported Features: Avoid functions like plot or input inside parfor.

5. Example Usage

Example 1: Summation with Reduction Variable

matlab
Copy
n = 1e6;
a = rand(n, 1);
total = 0;
parfor i = 1:n
    total = total + a(i); % 'total' is a reduction variable
end

Example 2: Independent Function Evaluation

matlab
Copy
results = zeros(100, 1);
parfor i = 1:100
    results(i) = sqrt(rand()); % Sliced variable 'results'
end

6. Performance Tips

  • Overhead Justification: Use parfor for computationally intensive iterations.
  • Preallocate Arrays: Avoid growing arrays inside loops.
  • Benchmark: Compare for and parfor execution times with tic/toc.

7. Debugging & Limitations

  • Debugging: Test loops with for first, then switch to parfor.
  • No Nested parfor: Use parfor only at the outermost loop.
  • Error Handling: Errors in iterations are reported after all iterations complete.

8. Advanced Features

  • Control Workers: Specify the number of workers with parpool(numWorkers).
  • Distributed Arrays: For large datasets, consider distributed arrays to manage memory.

When to Use parfor

  • Suitable: Independent tasks (e.g., simulations, batch processing).
  • Unsuitable: Data-dependent loops, quick iterations, or non-parallelizable functions.

By adhering to these guidelines, you can effectively leverage parfor to accelerate MATLAB computations through parallel processing.

Published by howdy