Benchmark: array vs float32array max

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 Float32Array(10000)).map(_ => Math.random(1000000));

Tests:

array max apply
Math.max.apply(Math, a);
typedArray max apply
Math.max.apply(Math, ta);
array max
Math.max(...a);
typedArray max
Math.max(...ta);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array max apply
typedArray max apply
array max
typedArray max

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark definition represents a JavaScript function that performs a specific operation on an array of numbers. There are four functions:

1. `Math.max.apply(Math, a);` - This function applies the `max` method to the entire array `a`.
2. `Math.max(...a);` - This function applies the `max` method to each element of the array `a` individually.
3. `(new Float32Array(10000)).map(_ => Math.random(1000000));` and similar function for typed arrays (`ta`).
4. `getRandomInt(max) { return Math.floor(Math.random() * max);}` - This is a helper function used to generate random numbers.

These functions are tested on both regular arrays (`a`) and typed arrays (`ta`, specifically Float32Array).

**Options being compared**

The options being compared are:

1. **`apply` method**: `Math.max.apply(Math, array)` vs `array.max`
2. **Native methods**: `Math.max(...array)` vs `array.max`

**Pros and Cons**

**Apply Method**

* Pros:
	+ Can be more efficient for very large arrays (since it doesn't create new intermediate results)
* Cons:
	+ Less intuitive and less readable
	+ Requires the `apply` method to be called explicitly

Native Methods

* Pros:
	+ More intuitive and easier to read
	+ Does not require calling an explicit method (`max`)
* Cons:
	+ May incur more overhead due to function calls (although this is unlikely to matter for most use cases)

**Typed Arrays**

Using typed arrays (e.g., `Float32Array`) can provide benefits in terms of memory usage and performance. However, this may not be significant enough to make a noticeable difference in this specific benchmark.

**Other Considerations**

* **Random Number Generation**: The benchmark uses random numbers generated by the `getRandomInt` function. This is used to populate the arrays with values.
* **Browser Variability**: The benchmark results are from different browsers and platforms, which can introduce variability due to differences in implementation.

**Alternative Approaches**

Some possible alternative approaches for this benchmark could include:

1. Using a more complex array or data structure (e.g., objects, sets) to make the optimization more challenging.
2. Adding additional operations or constraints to the benchmark (e.g., sorting, searching).
3. Increasing the size of the arrays or using different types of data structures.

Keep in mind that these alternatives would require careful consideration of the trade-offs between readability, performance, and maintainability.

Latest run results:

Run details: (Test run date: 4 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36

Browser/OS: Chrome 126 on Windows

View result in a separate tab

Test name	Executions per second
array max apply	102101.2 Ops/sec
typedArray max apply	3769.0 Ops/sec
array max	15071.9 Ops/sec
typedArray max	2160.2 Ops/sec

Related benchmarks:

array vs float32array max (version: 0)

Comparing performance of: array max apply vs typedArray max apply vs array max vs typedArray max

Created: 4 months ago by: Guest

Jump to the latest result

array max apply

typedArray max apply

array max

typedArray max

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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