Benchmark: find half some 3

Tests:

half
const data = new Array(1000).fill(null).map((n, i) => i); const bisectHalf = (arr, cv) => { let step = Math.ceil(arr.length / 2); let index = step; while (step > 1) { if ( arr[step] === cv || (cv > arr[index] && arr[index + 1] && cv < arr[index + 1]) ) { step = 0; } step = Math.ceil(step / 2); if (cv > arr[index]) { index += step; } else { index -= step; } } return arr[index]; } for(var i = 0; i < 1000; i++) { bisectHalf(data, i); }
some
const data = new Array(1000).fill(null).map((n, i) => i); const bisectSome = (arr, cv) => { let index = -1; arr.some((el, i) => { if (el < cv) { return false; } else { index = i - 1; return true; } }); return arr[index]; }; for(var i = 0; i < 1000; i++) { bisectSome(data, i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
half
some

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark and explain what's being tested, compared options, pros and cons, and other considerations.

**Benchmark Definition**

The provided `Benchmark Definition` is a JSON object that defines two test cases: "half" and "some". It contains a JavaScript script preparation code for each test case.

In this context, a **benchmark** refers to a set of instructions designed to measure the performance of a particular piece of code or algorithm. In this case, the benchmark tests the execution time of two different algorithms for finding an element in an array: binary search (`bisectHalf`) and linear search (`bisectSome`).

**Options Compared**

The benchmark compares the following options:

1. **Binary Search (bisectHalf)**: This is a fast search algorithm that finds an element in a sorted array by repeatedly dividing the search interval in half.
2. **Linear Search (bisectSome)**: This is a simple search algorithm that checks each element in the array until it finds a match.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Binary Search**
	+ Pros:
		- Fastest for large datasets
		- O(n log n) time complexity
	+ Cons:
		- Requires the data to be sorted, which can add extra overhead
		- May not work well for small or unsorted datasets
* **Linear Search**
	+ Pros:
		- Simple to implement and understand
		- Works with unsorted or small datasets
	+ Cons:
		- Slowest for large datasets (O(n) time complexity)
		- May not be efficient for repeated searches

**Library**

In the provided benchmark definition, both algorithms use an external library: `Array.prototype.some()`. This method returns `true` if at least one element in the array passes the test implemented by its callback function. In this case, it's used to find the index of the target value (`i`) in the sorted array.

**Special JS Features**

The benchmark definition uses a JavaScript feature called **arrow functions**, which were introduced in ECMAScript 2015 (ES6). Arrow functions provide a concise way to define small, single-expression functions. In this case, they're used to define the `bisectHalf` and `bisectSome` algorithms.

**Other Considerations**

The benchmark definition also includes some setup code for each test case:

* For "half", it creates a sorted array with 1000 elements filled with null values using `Array.prototype.fill()` and then maps over the array to create an index-based representation of the data.
* For "some", it's similar, but uses `Array.prototype.some()` instead of mapping.

**Alternatives**

There are other alternatives to binary search and linear search, such as:

* **Hash Tables**: Can provide O(1) lookups for certain types of searches (e.g., searching for a specific key in a hash table).
* **Ternary Search**: An algorithm that combines two-way search with three-way search to find an element more efficiently.
* **Exponential Search**: A search algorithm that uses binary search but starts at the correct position rather than the middle.

However, these alternatives may not be as straightforward to implement or understand as binary search and linear search.

Related benchmarks:

find half some 3 (version: 0)

Comparing performance of: half vs some

Created: 5 years ago by: Guest

Jump to the latest result

half

some

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):