Benchmark: array manip31 - MeasureThat.net

Script Preparation code:

arr = [];
for (i = 0; i < 1600; i++) {
    arr[i] = i % 20
}
testArray = new Uint8Array(arr);

Tests:

At once
p = Array.from(testArray); while(p.length > 160) { discard = p.slice(0, 160); p = p.slice(160); }
in loop
while(testArray.length > 160) { discard = Array.from(testArray.subarray(0, 160)); testArray = testArray.subarray(160); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
At once
in loop

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 JSON data and explain what's being tested.

**Benchmark Definition**

The benchmark is measuring the performance of two approaches to manipulate an array:

1. **"array manip31"**: This is the input data that defines the array manipulation task. It creates a Uint8Array with 1600 elements, where each element is the remainder of its index divided by 20.
2. **Script Preparation Code**: The script prepares the test environment by creating the `arr` array and assigning it to the `testArray` variable.
3. **Html Preparation Code**: This field is empty, indicating that no HTML-related preparation code is needed.

**Individual Test Cases**

There are two test cases:

1. **"At once"`**: This test case uses the `Array.from()` method to create a new array from the `testArray`. It then enters a loop where it slices the original array into two parts: `discard` and `p`. The loop continues until the length of `p` is less than or equal to 160. This approach is often referred to as "all-at-once" or "batching".
2. **"in loop"`**: This test case uses a while loop to iterate over the `testArray`. Inside the loop, it uses `Array.from()` and `subarray()` methods to slice the array into two parts: `discard` and `testArray`. The loop continues until the length of `testArray` is less than or equal to 160. This approach is often referred to as "iterative" or "incremental".

**Comparison**

The comparison being made here is between the "all-at-once" (At once) and iterative (in loop) approaches to array manipulation.

**Pros and Cons of Each Approach**

1. **All-at-once (At once)**:
	* Pros:
		+ Can be more efficient due to reduced overhead from iteration.
		+ May exhibit better cache locality, leading to better performance.
	* Cons:
		+ May require more memory allocation and deallocation.
		+ Can lead to slower initial warm-up times.
2. **Iterative (in loop)**:
	* Pros:
		+ Typically has lower overhead from iteration.
		+ Can exhibit better cache locality and performance in certain scenarios.
	* Cons:
		+ May require more iterations, leading to slower overall performance.

**Library and Special JS Feature**

In this benchmark, the `Array.from()` method is used. This method was introduced in ECMAScript 2015 (ES6) as a way to create an array from an iterable or an array-like object. It's a convenient way to perform conversions between different data types.

No special JavaScript features are used beyond what's required for ES6 syntax, such as `let` and `const` declarations.

**Alternatives**

If you're looking for alternative approaches to measure array manipulation performance, here are some options:

1. **Manual looping**: Implement the same logic using a traditional while loop or for loop.
2. **Other libraries or frameworks**: Consider using a different library or framework that provides optimized array manipulation functions, such as jQuery or a custom implementation.
3. **Native array methods**: Experiment with other native array methods, like `map()`, `filter()`, and `reduce()`, to see if they provide better performance in this specific scenario.

Keep in mind that the choice of approach depends on your specific use case and requirements.

Related benchmarks:

array manip31 (version: 0)

s

Comparing performance of: At once vs in loop

Created: 3 years ago by: Guest

Jump to the latest result

At once

in loop

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):