Benchmark: Math.max vs Array.reduce

Script Preparation code:

var massiveArray = new Array(100000).fill().map(e => Math.random())

Tests:

Math.min
Math.max(...massiveArray)
Array.reduce
massiveArray.reduce((a, b) => a > b ? a : b)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.min
Array.reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:141.0) Gecko/20100101 Firefox/141.0

Browser/OS: Firefox 141 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Math.min	1758.8 Ops/sec
Array.reduce	3233.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its test cases.

**Benchmark Overview**

The benchmark compares the performance of two JavaScript functions: `Math.max` and `Array.prototype.reduce`. The goal is to determine which function is faster for large arrays.

**Options Compared**

The benchmark tests two approaches:

1. **Math.max**: This function takes multiple arguments and returns the largest value. It's a built-in JavaScript function that uses the ECMAScript standard.
2. **Array.prototype.reduce**: This method applies a reduction function to each element of an array, accumulating a result. In this case, the reduction function compares two elements using the greater-than operator (`a > b`) and returns the larger value.

**Pros and Cons**

* **Math.max**:
	+ Pros: Simple, straightforward implementation; built-in function.
	+ Cons: May be slower for large arrays due to the need to call multiple times with different values; uses more memory as it creates an array of intermediate results.
* **Array.prototype.reduce**:
	+ Pros: Flexible and powerful reduction function; can be used for various operations beyond just finding the maximum value.
	+ Cons: May be slower than `Math.max` due to the overhead of creating a new accumulator variable and iterating through the array.

**Other Considerations**

In addition to the two main functions, there is another option mentioned in the benchmark setup code:

* **new Array(100000).fill().map(e => Math.random())**: This line creates an extremely large array with random values. The purpose of this step is likely to ensure that the benchmark runs with a sufficient amount of data for accurate comparisons.

**Library and Special JS Features**

There are no libraries or special JavaScript features mentioned in the provided code snippet. However, the use of `Array.prototype.reduce` does introduce some nuances related to array methods:

* The `reduce()` method is a part of the ECMAScript standard.
* It's implemented as an array method on the `Array` prototype.
* It uses a closure to capture variables from its surrounding scope.

**Alternative Approaches**

Other ways to find the maximum value in a large array might include:

1. **Using a different reduction function**: Instead of comparing values directly, you could use a different comparison operation (e.g., finding the minimum value).
2. **Parallelizing the computation**: If you have access to multiple CPU cores or threads, you could divide the array into smaller chunks and process them concurrently using techniques like parallel loops or worker threads.
3. **Using a dedicated library or framework**: Depending on your specific requirements, there might be libraries (e.g., `lodash`) or frameworks that provide optimized functions for finding maximum values in arrays.

Keep in mind that these alternative approaches may introduce additional overhead, complexity, or dependencies not accounted for in the benchmark setup code provided.

Related benchmarks:

Math.max vs Array.reduce (version: 0)

Test whether Math.max or Array.reduce is faster for large number arrays

Comparing performance of: Math.min vs Array.reduce

Created: 4 years ago by: Guest

Jump to the latest result

Math.min

Array.reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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