Benchmark: loop vs reduce for summation

Script Preparation code:

window.array = [];
for(let i = 0; i < 1000000; i++) {
  window.array.push(i);
}

Tests:

loop
let sum = 0; for(let i = 0; i < array.length; i++) { sum += window.array[i]; }
reduce
window.array.reduce((acc, val) => acc + val );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
loop
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
loop	3.5 Ops/sec
reduce	63.5 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is crucial to optimize code and improve user experience.

Let's break down the provided benchmark definition:

**Benchmark Definition**

The benchmark measures the performance difference between using a traditional `for` loop and the `reduce()` method for calculating the sum of an array. The array contains 1,000,000 elements, which is a large dataset to test performance.

**Options Compared**

Two options are compared:

1. **Traditional `for` loop**: This approach uses a manual incrementing counter (`i`) and accesses each element in the array using indexing (`window.array[i]`). This method requires explicit looping and conditional checks.
2. **`reduce()` method**: The `reduce()` method is called with an initial value of 0, which accumulates the sum of all elements in the array. The callback function takes two arguments: the accumulator (`acc`) and the current element (`val`).

**Pros and Cons**

1. **Traditional `for` loop**:
	* Pros: Easy to understand, no additional libraries or syntax required.
	* Cons: Can be slower due to indexing and incrementing counter overhead.
2. **`reduce()` method**:
	* Pros: More concise and expressive code, can reduce memory allocation and copying overhead.
	* Cons: Requires knowledge of the `reduce()` method and its callback function.

The choice between these two approaches depends on the specific use case, team familiarity, and personal preference. However, for performance-critical code, the `reduce()` method might be a better option due to its concise nature and reduced overhead.

**Library**

In this benchmark, the `window.array` variable represents an array in the context of the benchmark script. The actual implementation is not shown, but it's likely a simple array with 1,000,000 elements initialized with integer values from 0 to 999,999.

**Special JS Feature/Syntax**

None mentioned explicitly. However, keep in mind that some browsers may optimize or interpret certain JavaScript features differently due to their implementation details (e.g., how they handle closures, scopes, or type conversions).

**Other Alternatives**

If the `reduce()` method is not supported by older browsers or specific environments, alternative approaches can be considered:

1. **`Array.prototype.forEach()`**: A more modern approach for iterating over arrays, which is compatible with most modern browsers.
2. **Manual array iteration using a counter**: Similar to the traditional `for` loop approach but without the need for explicit indexing.
3. **Other accumulation functions**: Depending on the specific use case, other accumulation functions like `Array.prototype.reduce()` or custom implementations can be used.

Please note that each of these alternatives has its own trade-offs and performance characteristics, which should be considered when choosing an implementation.

If you'd like to explore more benchmarking options or discuss further optimizations, feel free to ask!

Related benchmarks:

loop vs reduce for summation (version: 0)

Comparing performance of: loop vs reduce

Created: 5 years ago by: Guest

Jump to the latest result

loop

reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):