Benchmark builder

Easily compare execution time of tasks with this very simple online benchmark builder.

Enable JavaScript to use the interactive tool. The guide below explains how it works.

Benchmark Builder: Comprehensive Performance Analysis Guide

1. What the Tool Does

The Benchmark Builder is a specialized utility designed for developers and systems engineers to aggregate, analyze, and compare performance data across multiple execution "suites." Unlike simple "stopwatch" timers, this tool provides a statistical framework for understanding the consistency and reliability of code execution.

The tool allows users to define multiple suites (representing different algorithms, hardware configurations, or environments) and input multiple data points (samples) for each. It then automatically computes:

• Mean (Average): The central tendency of the performance data.
• Variance: A measure of how much the data points deviate from the mean, indicating the "noise" or instability of the test environment.
• Relative Comparison: Automatic calculation of the delta (difference) and ratio (multiplier) between the best-performing suite and all others.

The output is presented in a clean, sorted table that highlights the "winner" and provides exportable formats for documentation or team communication.

2. Why Someone Uses It

Performance measurement is notoriously difficult due to environmental noise, background processes, and hardware variability. This tool addresses several critical needs:

Eliminating "One-Off" Fallacies

A single execution of a function might be fast due to a lucky cache hit, or slow due to a background system update. By requiring multiple samples, the Benchmark Builder forces developers to look at the average behavior, which is a much more reliable predictor of real-world performance.

Quantifying Environmental Noise

The Variance calculation is one of the tool's most powerful features. A high variance indicates that the results are inconsistent. This might signal that the benchmarking environment is "noisy" (other apps are running), the CPU is thermal throttling, or the code being tested has unpredictable execution paths.

Objective Comparison

When choosing between two libraries or two versions of an algorithm, developers need more than a "feeling." This tool provides hard numbers like "Algorithm B is 1.4x slower than Algorithm A," making architectural decisions defensible.

3. Step-by-Step Instructions

Step 1: Define Your Suites

Start by naming your suites. For example, if you are comparing two sorting algorithms, name them "QuickSort" and "MergeSort." By default, the tool provides two suites to get you started.

Step 2: Input Sample Data

For each suite, enter the timing results of your tests. These should ideally be the result of multiple independent runs.

• Pro Tip: Use a consistent unit (like milliseconds or microseconds) for all values.
• You can add or remove suites using the "Add suite" and "Delete suite" buttons.

Step 3: Set the Unit

Enter the unit of measurement (e.g., ms, μs, ops/sec) in the "Unit" field. This unit will be applied to all calculations and reflected in the comparison strings.

Step 4: Analyze Results

Observe the results table. The tool automatically sorts the suites from fastest (top) to slowest (bottom).

• Look at the Mean to see which is fastest.
• Look at the Variance to see which is most stable.
• Check the Comparison column to see exactly how much slower the other suites are compared to the leader.

Step 5: Export Data

Use the "Copy as markdown table" button to generate a formatted table for your GitHub PR or README. Alternatively, use "Copy as bullet list" for a quick summary in a Slack or Discord message.

4. Examples

Scenario: Comparing String Concatenation

Suite 1 (String +): [150, 155, 148, 160, 152]
Suite 2 (Array.join): [90, 92, 88, 95, 91]

Result Table:

1. Array.join: Mean: 91.2ms (Best)
2. String +: Mean: 153ms (+61.8ms; x1.68)

5. Technical Background: Statistics in Benchmarking

To get the most out of this tool, it is helpful to understand the math behind it.

The Mean vs. The Median

While this tool focuses on the Arithmetic Mean, it is important to remember that the mean can be skewed by "outliers" (values that are much higher or lower than the rest). If you have one sample that is 1000ms while others are 10ms, your mean will be significantly higher. Best Practice: Always run enough samples (at least 10-20) to ensure the mean is a fair representation of the data.

Variance and Stability

Variance is calculated by taking the average of the squared differences from the Mean. In simple terms:

• Low Variance: Your code is predictable.
• High Variance: Your environment is unstable. This usually happens if you are benchmarking on a laptop that is also running Chrome, Spotify, and Slack in the background.

6. Common Mistakes

• Mixing Units: Entering 0.5 (seconds) in one suite and 500 (milliseconds) in another. The tool assumes all numbers are in the same scale.
• Insufficient Samples: Using only 2 or 3 samples. This is rarely enough to overcome the noise of modern operating systems.
• Comparing Incomparable Scenarios: Benchmarking a "cold" function (first run, no JIT optimization) against a "warm" function (run 1000 times before). Always "warm up" your code before taking the samples you input into this tool.
• Thermal Throttling: Running long benchmarks on a battery-powered device. As the CPU gets hot, it will slow down, causing later suites to look artificially slower than earlier ones.

7. Use Cases

Library Evaluation

Comparing the performance of lodash vs. native JavaScript methods vs. ramda. You can run the same operation with each library and input the results here to see the overhead.

CI/CD Regression Analysis

If a PR is suspected of slowing down a critical path, you can benchmark the main branch vs. the feature branch and paste the resulting Markdown table directly into the PR comments.

Hardware Comparison

Testing the same code on an Intel i7 vs. an Apple M2. Use the suites to represent the different hardware and input the execution times to see the efficiency gains.

Optimization Validation

Before and after refactoring a complex loop. This tool provides the "before/after" proof that your optimization actually worked.

8. Related Tools

• Chronometer: For manual timing of events that you can then input into the Benchmark Builder.
• JSON Formatter: For cleaning up large sets of raw timing data from logs before extraction.
• Device Information: Useful for documenting the environment (browser, OS, screen) where the benchmark was performed.

Technical Note: The Benchmark Builder uses a reactive calculation engine. All statistical operations (summation, averaging, variance calculation) are performed in real-time as you type, ensuring that you can immediately see the impact of adding a new data point.