Benchmark: array vs float64 for io and slice (fixed)

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}
var a = [...Array(10000)].map(_ => Math.random(1000000));
var ta = (new Float64Array(10000)).map(_ => Math.random(1000000));

Tests:

array slice
a.slice(0);
typedArray slice
ta.slice(0);
array i/o
for (let i = 0; i < 10000; ++i) { a[i] = a[i] + 1; }
typedArray i/o
for (let i = 0; i < 10000; ++i) { ta[i] = ta[i] + 1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array slice
typedArray slice
array i/o
typedArray i/o

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

**Overview**

The provided JSON represents a benchmark test case created on MeasureThat.net, a website where users can create and run JavaScript microbenchmarks. The test compares the performance of arrays and TypedArrays in various operations.

**Benchmark Definition**

The benchmark definition is represented by two JSON objects:

1. **"Name": "array vs float64 for io and slice (fixed)"**, which describes the overall purpose of the benchmark.
2. **Individual test cases**: Each test case has a unique name, a benchmark definition, and an optional script preparation code or HTML preparation code.

**Test Cases**

There are four individual test cases:

1. **"array slice"`**: Tests the performance of slicing arrays using the `slice()` method.
2. **"typedArray slice"`**: Tests the performance of slicing TypedArrays using the `slice()` method.
3. **"array i/o"`**: Tests the performance of iterative input/output operations on arrays, where each element is incremented by 1.
4. **"typedArray i/o"`**: Tests the performance of iterative input/output operations on TypedArrays, where each element is incremented by 1.

**Options Compared**

The benchmark compares two options:

* **Arrays**:
	+ **Pros**: Arrays are a built-in JavaScript data structure and have better support for various methods.
	+ **Cons**: Arrays may have slower performance due to their dynamic nature and memory management overhead.
* **TypedArrays**:
	+ **Pros**: TypedArrays are optimized arrays that provide better performance, especially when working with large datasets or numerical computations. They also offer type safety and can reduce memory usage compared to regular arrays.
	+ **Cons**: TypedArrays may require additional setup and configuration, and their support for certain methods is limited.

**Library Usage**

In the provided benchmark definition, no external libraries are used. However, in some test cases, JavaScript built-in functions like `Array.prototype.slice()` and `Float64Array` are used.

**Special JS Features or Syntax**

No special JavaScript features or syntax (e.g., async/await, promises) are used in this benchmark. The focus is on comparing the performance of arrays and TypedArrays.

**Other Alternatives**

If you need to create similar benchmarks for other operations or data structures, consider using MeasureThat.net's website or alternatives like:

* Microbenchmarking libraries like `micro-benchmark` or `Benchmark.js`
* Online platforms like Google's `jsperf` (now deprecated) or Mozilla's `SpiderMonkey Tuner`

Keep in mind that each platform has its strengths and weaknesses, and some might be better suited for your specific use case.

Latest run results:

Run details: (Test run date: 9 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:132.0) Gecko/20100101 Firefox/132.0

Browser/OS: Firefox 132 on Windows

View result in a separate tab

Test name	Executions per second
array slice	32700.3 Ops/sec
typedArray slice	37947.1 Ops/sec
array i/o	114456.4 Ops/sec
typedArray i/o	193541.1 Ops/sec

Related benchmarks:

array vs float64 for io and slice (fixed) (version: 0)

Comparing performance of: array slice vs typedArray slice vs array i/o vs typedArray i/o

Created: one year ago by: Guest

Jump to the latest result

array slice

typedArray slice

array i/o

typedArray i/o

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):