Benchmark: reduce vs loop - MeasureThat.net

Script Preparation code:

t=new Array(500000);
for (let i=0;i<t.length;i++) {t[i]=Math.random();}

Tests:

reduce
t.reduce((p,c) => p+c, 0);
loop
(() => { let i = 0, res = 0, l = t.length; for (; i < l; i++) res += t[i]; return res; })();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
loop

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Definition and Preparation Code**

The benchmark is defined by two scripts: `Script Preparation Code` and `Html Preparation Code`. The former initializes an array `t` with 500,000 elements, each containing a random number between 0 and 1. This array will be used as input for the benchmark tests.

**Options Compared**

The benchmark compares two approaches:

1. **Reduce Method**: The first test case uses the `Array.prototype.reduce()` method to sum up all elements in the array `t`. This method applies a reduction function (in this case, adding 0 to each element and then summing them up) to an array.
2. **Loop**: The second test case uses a traditional loop to iterate over the array `t` and add up its elements.

**Pros and Cons of Each Approach**

1. **Reduce Method**:
	* Pros: concise, efficient (especially for large arrays), and expressive.
	* Cons: may have higher memory overhead due to the creation of an intermediate accumulator object.
2. **Loop**:
	* Pros: easy to understand, no additional memory allocation required, and can be more cache-friendly.
	* Cons: more verbose, less concise, and potentially slower for large arrays.

**Library Used**

The benchmark uses the `Array` prototype methods, specifically `reduce()` and `length`, which are built-in JavaScript library functions. The purpose of these methods is to provide a way to work with arrays in a concise and expressive manner.

**Special JS Feature or Syntax**

There is no special JavaScript feature or syntax used in this benchmark beyond what's mentioned above (i.e., the use of arrow functions for the `reduce()` method).

**Other Alternatives**

If you were to implement a similar benchmark, you could also consider using other approaches, such as:

1. **Using `for...of` loop**: Instead of a traditional `for` loop, you could use an `Array.prototype.forEach()` call with an arrow function to iterate over the array.
2. **Using `map()` and `reduce()` chaining**: You could create two intermediate arrays using `map()` and then chain another `reduce()` call to sum up their elements.
3. **Using a custom iterator**: If you wanted to explore alternative iteration mechanisms, you could define a custom iterator class or use a library like `iterator-js` to implement an iterative algorithm.

Keep in mind that the choice of approach will depend on your specific requirements and the characteristics of your data.

**Benchmark Preparation Code**

The provided `Script Preparation Code` initializes an array `t` with 500,000 random numbers, which is then used as input for the benchmark tests. The `Html Preparation Code` is empty in this case, indicating that no HTML-related preparation code was needed for this benchmark.

I hope this explanation helps you understand the benchmark and its options!

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2486.0 Safari/537.36 Edge/13.10586

Browser/OS: Edge 13.10586 on Windows

View result in a separate tab

Test name	Executions per second
reduce	110.9 Ops/sec
loop	11.4 Ops/sec

Related benchmarks:

reduce vs loop (version: 0)

Comparing performance of: reduce vs loop

Created: 8 years ago by: Registered User

Jump to the latest result

reduce

loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):