JS Benchmark Runner

Compare JavaScript code performance — measure execution speed of different implementations side by side

JavaScript Benchmark Runner — Measure and Compare Code Performance with High-Precision Timing, Side-by-Side Test Cases, and Historical Tracking

1. Write optional Setup Code that prepares shared data or state for all test cases — this code runs once before each test begins and is not included in the timing measurement. For example, pre-generate a large array (const data = [...Array(10000).keys()]), create complex object hierarchies, or initialize Web API resources. Keeping setup code shared and excluded from timing ensures that your performance measurements reflect only the algorithm under test, not data preparation overhead. The setup block accepts any valid JavaScript — you have full access to the browser's JS engine including modern ES2024 features.
2. Add one or more Test Cases — each consists of a descriptive name (like 'for loop', 'Array.forEach', or 'for...of') and the code snippet to benchmark. Arrange competing implementations side by side — for example, compare the performance of Array.filter().map() against a single for loop that does both operations, or benchmark JSON.parse() against a custom streaming parser, or compare different sorting algorithms on the same dataset. The tool runs each test case independently with the same number of iterations, ensuring fair comparison between implementations.
3. Configure the benchmark parameters: Iterations controls how many times each test case executes per run (higher values produce more stable averages by amortizing browser JIT compilation costs), Warmup Runs executes preliminary rounds to trigger JIT optimization before measurement begins (essential for comparing V8-optimized code paths), and Timeout caps total execution time to prevent infinite loops. Click Run Benchmark to execute all test cases and view results including operations per second (ops/sec), total execution time, average time per operation, and relative speed compared to the fastest implementation. Use Re-run All to execute thrice for statistical confidence. Benchmark history is saved locally for tracking optimization progress over time.

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