/matlab-performance-optimizer

name: matlab-performance-optimizer
description: Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.
license: MathWorks BSD-3-Clause (see LICENSE)
metadata:
  author: MathWorks
  version: "1.1"

MATLAB Performance Optimizer

Optimize MATLAB code performance with vectorization, memory management, and profiling tools.

When to Use This Skill

• Optimizing slow or inefficient MATLAB code
• Converting loops to vectorized operations
• Reducing memory usage
• Improving algorithm performance
• When user mentions: slow, performance, optimize, speed up, efficient, memory
• Profiling code to find bottlenecks
• Parallelizing computations

Core Optimization Principles

1. Vectorization (Most Important)

**Replace loops with vectorized operations whenever possible.**

**SLOW - Using loops:**

% Slow approach
n = 1000000;
result = zeros(n, 1);
for i = 1:n
    result(i) = sin(i) * cos(i);
end

**FAST - Vectorized:**

% Fast approach
n = 1000000;
i = (1:n).';
result = sin(i) .* cos(i);

2. Preallocate Arrays

**Always preallocate arrays before loops.**

**SLOW - Growing arrays:**

% Very slow - array grows each iteration
result = [];
for i = 1:10000
    result(end+1) = i^2;
end

**FAST - Preallocated:**

% Fast - preallocated array
n = 10000;
result = zeros(n, 1);
for i = 1:n
    result(i) = i^2;
end

3. Use Built-in Functions

**MATLAB built-in functions are highly optimized.**

**SLOW - Manual implementation:**

% Slow
sum_val = 0;
for i = 1:length(x)
    sum_val = sum_val + x(i);
end

**FAST - Built-in function:**

% Fast
sum_val = sum(x);

Vectorization Techniques

Element-wise Operations

Use `.*`, `./`, `.^` for element-wise operations:

% Instead of this:
for i = 1:length(x)
    y(i) = x(i)^2 + 2*x(i) + 1;
end

% Do this:
y = x.^2 + 2*x + 1;

Logical Indexing

Replace conditional loops with logical indexing:

% Instead of this:
count = 0;
for i = 1:length(data)
    if data(i) > threshold
        count = count + 1;
        filtered(count) = data(i);
    end
end
filtered = filtered(1:count);

% Do this:
filtered = data(data > threshold);

Matrix Operations

Use matrix multiplication instead of nested loops:

% Instead of this:
C = zeros(size(A, 1), size(B, 2));
for i = 1:size(A, 1)
    for j = 1:size(B, 2)
        for k = 1:size(A, 2)
            C(i,j) = C(i,j) + A(i,k) * B(k,j);
        end
    end
end

% Do this:
C = A * B;

Cumulative Operations

Use `cumsum`, `cumprod`, `cummax`, `cummin`:

% Instead of this:
running_sum = zeros(size(data));
running_sum(1) = data(1);
for i = 2:length(data)
    running_sum(i) = running_sum(i-1) + data(i);
end

% Do this:
running_sum = cumsum(data);

Memory Optimization

Use Appropriate Data Types

% Instead of default double (8 bytes)
data = rand(1000, 1000);  % 8 MB

% Use single precision when appropriate (4 bytes)
data = single(rand(1000, 1000));  % 4 MB

% Use integers when applicable
indices = uint32(1:1000000);  % 4 MB instead of 8 MB

Sparse Matrices

For matrices with mostly zeros:

% Dense matrix (wastes memory)
A = zeros(10000, 10000);
A(1:100, 1:100) = rand(100);  % 800 MB

% Sparse matrix (efficient)
A = sparse(10000, 10000);
A(1:100, 1:100) = rand(100);  % Only stores non-zeros

Clear Unused Variables

% Process large data
largeData = loadData();
processedData = processData(largeData);

% Clear when no longer needed
clear largeData;

% Continue with processed data
results = analyze(processedData);

In-Place Operations

% Instead of creating copies
A = A + 5;  % In-place when possible

% Avoid unnecessary copies
B = A;      % Creates copy if A is modified later
B = A + 0;  % Forces copy

Profiling and Benchmarking

Using the Profiler

% Profile code execution
profile on
myFunction(inputs);
profile viewer
profile off

The profiler shows:

• Time spent in each function
• Number of calls to each function
• Lines that take the most time

Timing Comparisons

% Time single execution
tic;
result = myFunction(data);
elapsedTime = toc;

% Benchmark with timeit (more accurate)
timeit(@() myFunction(data))

% Compare multiple approaches
time1 = timeit(@() approach1(data));
time2 = timeit(@() approach2(data));
fprintf('Approach 1: %.6f s\nApproach 2: %.6f s\n', time1, time2);

Common Optimization Patterns

Pattern 1: Replace find with Logical Indexing

% SLOW
indices = find(x > 5);
y = x(indices);

% FAST
y = x(x > 5);

Pattern 2: Use Implicit Expansion Instead of repmat

% SLOW - repmat to match dimensions
A = rand(1000, 5);
B = rand(1, 5);
C = A - repmat(B, size(A, 1), 1);

% FAST - implicit expansion (R2016b+)
C = A - B;

Pattern 3: Avoid Repeated Calculations

% SLOW - recalculates each iteration
for i = 1:n
    result(i) = data(i) / sqrt(sum(data.^2));
end

% FAST - calculate once
norm_factor = sqrt(sum(data.^2));
for i = 1:n
    result(i) = data(i) / norm_factor;
end

% EVEN FASTER - vectorize
result = data / sqrt(sum(data.^2));

Pattern 4: Efficient String Operations

% SLOW - concatenating in loop
str = '';
for i = 1:1000
    str = [str, sprintf('Line %d\n', i)];
end

% FAST - cell array + join
lines = cell(1000, 1);
for i = 1:1000
    lines{i} = sprintf('Line %d', i);
end
str = strjoin(lines, '\n');

% FASTEST - vectorized sprintf
str = sprintf('Line %d\n', 1:1000);

Pattern 5: Use Table for Mixed Data Types

% Instead of separate arrays
names = cell(1000, 1);
ages = zeros(1000, 1);
scores = zeros(1000, 1);

% Use table
data = table(names, ages, scores);
% Faster access and better organiz