Benchmark: test rapidite - MeasureThat.net

Tests:

func 1
const input = [ 1, 4, 5, 7, 8, 8, 11, 2, 14, 13, 12] const twoSum = (array, goal) => { let indexes = []; let count = 0; for(let i = 0; i < array.length; i++){ for(let j = i + 1; j < array.length; j++){ count++; if (array[i] + array[j] === goal) { indexes.push(i); indexes.push(j); console.log("Func 1 => " + count); return indexes; } } } return indexes; } console.log(twoSum(input, 18));
func 2
const input = [ 1, 4, 5, 7, 8, 8, 11, 2, 14, 13, 12] const twoSum2 = (array, goal) => { let mapOfNumbers = {}; let twoIndexes = []; let count = 0; for (let i = 0; i < array.length; i++) { mapOfNumbers[array[i]] = i; } for (let i = 0; i < array.length; i++) { count++; let target = goal - array[i]; if(mapOfNumbers[target] !== i && mapOfNumbers[target] !== undefined) { twoIndexes.push(i); twoIndexes.push(mapOfNumbers[target]); console.log("Func 2 => " + count); return twoIndexes; } } console.log("Func 2 => " + count); return twoIndexes; } console.log(twoSum2(input, 18));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
func 1
func 2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Overview**

The provided JSON represents two test cases for measuring JavaScript performance. Both tests are designed to measure the execution time of a two-sum algorithm, which takes an array of numbers as input and returns the indices of all pairs of numbers that add up to a given target sum (in this case, 18).

**Test Cases**

There are two individual test cases:

1. **`func 1`**: This test uses a brute-force approach with nested loops to find the pairs of numbers that add up to the target sum.
2. **`func 2`**: This test uses a hash table (object) to store the indices of each number in the array, and then uses another loop to find the pairs of numbers that add up to the target sum.

**Options Compared**

The two tests compare the performance of two different approaches:

1. Brute-force approach with nested loops (`func 1`)
2. Hash table-based approach with a single loop (`func 2`)

**Pros and Cons of Each Approach**

1. **Brute-Force Approach (func 1)**:
	* Pros:
		+ Easy to understand and implement
		+ No additional data structures required
	* Cons:
		+ Has a time complexity of O(n^2), which can be slow for large input sizes
		+ May not perform well on modern JavaScript engines that optimize loops
2. **Hash Table-Based Approach (func 2)**:
	* Pros:
		+ Has a time complexity of O(n), making it more efficient than the brute-force approach
		+ Reduces the number of iterations required to find pairs, resulting in faster execution times
	* Cons:
		+ Requires additional memory to store the hash table
		+ May have slower initial startup times due to the overhead of creating and populating the hash table

**Library Used**

Neither test case uses a specific library. However, both tests assume that the `console` object is available, which is a built-in object in JavaScript.

**Special JS Feature or Syntax**

There are no special features or syntax used in these test cases. They rely on standard JavaScript concepts and data structures.

**Other Considerations**

When running benchmarks like this, it's essential to consider factors such as:

* Input size: The size of the input array can significantly impact performance.
* Browser and engine versions: Different browsers and engines may optimize loops or handle memory allocation differently, affecting benchmark results.
* Cache locality: Modern JavaScript engines often use caching mechanisms to improve performance. However, these caches may not always be optimal for all test cases.

**Alternative Approaches**

Other possible approaches that could be used in a two-sum algorithm include:

* Using a more efficient data structure, such as a balanced binary search tree or a tries data structure
* Leveraging advanced JavaScript features like WebAssembly or SIMD instructions
* Employing parallel processing techniques to take advantage of multi-core processors

Keep in mind that these alternative approaches may require additional development and testing efforts but can potentially offer significant performance improvements.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:93.0) Gecko/20100101 Firefox/93.0

Browser/OS: Firefox 93 on Windows

View result in a separate tab

Test name	Executions per second
func 1	33551.1 Ops/sec
func 2	31678.0 Ops/sec

Related benchmarks:

test rapidite (version: 0)

Comparing performance of: func 1 vs func 2

Created: 3 years ago by: Guest

Jump to the latest result

func 1

func 2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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