Benchmark: array vs float64 for io and slice

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; } let tb = Array.from(ta);

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.

I'd be happy to explain the benchmark being tested on MeasureThat.net.

**Benchmark Definition**
The benchmark is designed to compare the performance of JavaScript arrays and TypedArrays (Float64Arrays) in various operations, specifically slicing and modifying elements.

**Test Cases**

There are four test cases:

1. `array slice`: Measures the time it takes to execute the `a.slice(0)` operation.
2. `typedArray slice`: Measures the time it takes to execute the `ta.slice(0)` operation.
3. `array i/o`: Measures the time it takes to modify elements in an array using a loop (`for (let i = 0; i < 10000; ++i) { a[i] = a[i] + 1; }`).
4. `typedArray i/o`: Measures the time it takes to convert a Float64Array to an Array and then modify elements in the resulting array (`for (let i = 0; i < 10000; ++i) { ta[i] = ta[i] + 1; } let tb = Array.from(ta);`).

**Comparison of Options**

In each test case, two options are compared:

* **Array**: The built-in JavaScript array type.
* **TypedArray (Float64Array)**: A typed array that represents a sequence of 64-bit floating-point numbers.

**Pros and Cons**

Here's a brief summary of the pros and cons of each option:

1. `array slice`:
	* Pros: Fast, widely supported, easy to use.
	* Cons: May have performance issues for very large datasets or complex operations.
2. `typedArray slice`:
	* Pros: Can provide better performance for certain types of data (e.g., floating-point numbers) and may be more memory-efficient.
	* Cons: Less common usage, may require additional setup and configuration.
3. `array i/o`:
	* Pros: Fast, widely supported, easy to use.
	* Cons: May have performance issues for very large datasets or complex operations.
4. `typedArray i/o`:
	* Pros: Can provide better performance for certain types of data (e.g., floating-point numbers) and may be more memory-efficient.
	* Cons: Requires additional setup (converting to an Array), and the conversion process can introduce overhead.

**Other Considerations**

When choosing between arrays and TypedArrays, consider the following factors:

* **Data type**: If you're working with floating-point numbers or other specialized data types, a TypedArray may be more suitable.
* **Performance**: If you need optimized performance for large datasets or complex operations, consider using a TypedArray.
* **Memory efficiency**: If memory is a concern, a TypedArray may be more efficient due to its ability to represent specific data types.

**Library and Special JS Features**

In the benchmark code, the following libraries are used:

* `Float64Array`: A typed array that represents a sequence of 64-bit floating-point numbers. This library is built-in to JavaScript.
* `Array.from()`: A method that converts an iterable object into an Array. This method is part of the modern JavaScript API.

No special JS features or syntax are used in this benchmark.

**Alternatives**

If you're looking for alternatives to MeasureThat.net, consider the following options:

* **Benchmarking libraries**: Libraries like Benchmark.js, Fast.js, or Microbenchmark allow you to write custom benchmarks and run them on different platforms.
* **WebAssembly**: WebAssembly provides a platform-agnostic way to execute optimized code in the browser, which can be useful for benchmarking performance-critical code.

Keep in mind that these alternatives may require more setup and configuration than MeasureThat.net.

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	34233.9 Ops/sec
typedArray slice	42052.5 Ops/sec
array i/o	119236.4 Ops/sec
typedArray i/o	10578.4 Ops/sec

Related benchmarks:

array vs float64 for io and slice (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):