Benchmark: Map vs Math Pow - MeasureThat.net

Script Preparation code:

function rowSumOddNumbers1(n) {
  return ([...Array(n)].map((x, i) => ( (n-1) * 2) * (n/2) + (i * 2) + 1)).reduce((a, b) => a + b);
}

function rowSumOddNumbers2(n) {
  return Math.pow(n, 3)
}

Tests:

Array Map
for (let i = 0; i < 1000; i++) { rowSumOddNumbers1(i); }
Math Pow
for (let i = 0; i < 1000; i++) { rowSumOddNumbers2(i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array Map
Math Pow

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is comparing two approaches for calculating the sum of odd numbers in an array: `rowSumOddNumbers1` (which uses JavaScript's built-in `Array.map` function) and `rowSumOddNumbers2` (which calculates the result manually using exponentiation).

**Options Compared**

Two options are being compared:

1. **`Array Map`**: This approach uses JavaScript's `Array.map` function to create a new array with the odd numbers, where each element is calculated as `(n-1) * 2 + (i * 2) + 1`. The `map` function applies this calculation to each element in the original array and returns an array of results. This approach then uses the `reduce` method to sum up all the elements in the resulting array.
2. **`Math Pow`**: This approach calculates the result manually using exponentiation, where `(n^3)`. The idea is to calculate the cube of `n`, which represents the number of odd numbers in each row (since there are `2n+1` odd numbers).

**Pros and Cons**

**Array Map:**

* Pros:
	+ Concise and readable code
	+ Leverages JavaScript's built-in `map` function, which is likely optimized for performance
	+ Easy to understand and maintain
* Cons:
	+ May have overhead due to the creation of a new array (although this can be mitigated with careful optimization)
	+ Requires an extra operation (reduction) to sum up all elements in the resulting array

**Math Pow:**

* Pros:
	+ Avoids creating a new array, which can reduce memory allocation and garbage collection overhead
	+ May be faster due to fewer operations required
* Cons:
	+ Code is more verbose and harder to understand, especially for those without experience with exponentiation or mathematical optimizations
	+ May not take advantage of CPU caching or other optimization opportunities

**Library**

In this case, there are no external libraries being used.

**Special JS Feature/Syntax**

There doesn't appear to be any special JavaScript feature or syntax being used in the benchmark. The code is written in standard ECMAScript 2015+ syntax.

**Alternatives**

Other approaches for calculating the sum of odd numbers might include:

* Using a `for` loop with manual indexing
* Employing a different mathematical formula that avoids exponentiation and array operations altogether (e.g., using bitwise operators)
* Utilizing specialized libraries or functions optimized for performance, such as those provided by a Just-In-Time (JIT) compiler or a GPU-accelerated library

Keep in mind that the specific approach used will depend on the requirements of the use case and the trade-offs between code readability, maintainability, and performance.

Related benchmarks:

Map vs Math Pow (version: 0)

Comparing performance of: Array Map vs Math Pow

Created: 7 years ago by: Guest

Jump to the latest result

Array Map

Math Pow

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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