To utilize parallel computing in MATLAB using parfor
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 withparfor
if none exists.
2. parfor
Basics
- Syntax: Replace
for
withparfor
in loop constructs.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
orinput
insideparfor
.
5. Example Usage
Example 1: Summation with Reduction Variable
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
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
andparfor
execution times withtic
/toc
.
7. Debugging & Limitations
- Debugging: Test loops with
for
first, then switch toparfor
. - No Nested
parfor
: Useparfor
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.