Benchmark: array vs Float64Array (small) 2

Script Preparation code:

var a = [...Array(100)].map(_ => Math.random());
var ta = (new Float64Array(100)).map(_ => Math.random());

Tests:

array sort
a.sort();
typedArray sort
ta.sort();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array sort
typedArray sort

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 days ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0

Browser/OS: Firefox 128 on Linux

View result in a separate tab

Test name	Executions per second
array sort	43861.7 Ops/sec
typedArray sort	671559.6 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

The provided JSON benchmark definition represents a test case that compares two approaches: using an array and using `Float64Array` to create a typed array. The "Script Preparation Code" section shows how these arrays are initialized with 100 random numbers each.

**What is tested?**

In this test, we're comparing the performance of sorting an array versus a `Float64Array`. Both data structures have a similar structure and use case, but they differ in their underlying representation. The goal is to determine which approach is faster for small datasets.

**Options compared:**

We have two main options being compared:

1. **Array**: A regular JavaScript array made of elements of type number (or other types depending on the context).
2. **Float64Array**: A typed array created from a buffer, optimized for fast arithmetic operations with 64-bit floating-point numbers.

**Pros and Cons:**

* **Array**:
	+ Pros:
		- Widely supported and easy to work with.
		- Can be easily converted between other data structures.
	+ Cons:
		- May incur overhead due to dynamic typing and boxing/unboxing operations.
		- Less efficient for large datasets or numerical computations.
* **Float64Array**:
	+ Pros:
		- Optimized for fast arithmetic operations with 64-bit floating-point numbers.
		- Can take advantage of SIMD (Single Instruction, Multiple Data) instructions.
	+ Cons:
		- Requires a typed array to be created from a buffer.
		- May not work well with other data types or operations.

**Library and its purpose:**

In the provided test case, the `Float64Array` is used, which is a built-in JavaScript type. Its primary purpose is to provide an efficient way to perform arithmetic operations on large numbers of 64-bit floating-point values.

**Special JS feature or syntax:**

There are no special features or syntaxes being tested in this benchmark. The focus is solely on comparing the performance of arrays and `Float64Array` for sorting small datasets.

**Other alternatives:**

If you're interested in exploring other options, here are a few:

* **Int8Array**, **Uint8Array**: These typed arrays provide optimized arithmetic operations for 8-bit integers.
* **BigInt**: Introduced in ECMAScript 2019, BigInt provides support for arbitrary-precision integers, which can be useful for cryptographic applications.

Keep in mind that these alternatives may not be directly relevant to this specific benchmark, but they demonstrate the diversity of options available in JavaScript for numerical computations.

Related benchmarks:

array vs Float64Array (small) 2 (version: 0)

Comparing performance of: array sort vs typedArray sort

Created: 3 years ago by: Guest

Jump to the latest result

array sort

typedArray sort

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):