Benchmark: Math.max - apply, call vs spread ~50K

Script Preparation code:

var length = Math.trunc(Math.random() * 2000) + 50000;

var arr = Array(length).map((_, i) => i);

Tests:

apply
Math.max.apply(null, arr);
call and spread
Math.max.call(null, ...arr);
spread
Math.max(...arr);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
apply
call and spread
spread

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 benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

The benchmark compares three different ways to call the `Math.max()` function in JavaScript: using the `apply()` method, the `call()` method with spread operator (`...`), and simply passing an array as a single argument (`...arr`). The goal is to determine which approach provides the best performance.

**Benchmark Definition JSON**

The benchmark definition JSON contains three main components:

1. **Script Preparation Code**: This code generates an array of random numbers, `length`, which will be used later in the benchmark.
2. **Html Preparation Code**: Since this is a JavaScript microbenchmark, there is no HTML preparation code required.
3. **Benchmark Definition**: This defines the core logic of the benchmark, which involves creating three test cases for each approach.

**Individual Test Cases**

Each test case is defined as an object containing:

1. **Benchmark Definition**: The actual code that will be executed in the browser.
	* For `apply()`, the code calls `Math.max()` with `null` as the first argument and `arr` as the second argument using the `apply()` method.
	* For `call and spread()`, the code calls `Math.max()` with `null` as the first argument and then spreads the `arr` array into the function using three dots (`...`).
	* For `spread()`, the code simply passes the `arr` array as a single argument to `Math.max()`.
2. **Test Name**: A descriptive name for each test case.

**Comparison**

The benchmark compares the performance of each approach across different browsers and devices. The comparison is likely based on execution speed (in seconds or milliseconds) per second (`ExecutionsPerSecond`).

**Pros and Cons of Each Approach**

1. **Apply()**
	* Pros: Simple and straightforward.
	* Cons: May require explicit type checking and handling for `null` or undefined arguments.
2. **Call with Spread Operator (`...`)]
	* Pros: Modern and concise, uses the spread operator to create an array.
	* Cons: May be less intuitive or familiar to some developers.
3. **Spread (Using Three Dots)**
	* Pros: Simple and readable, similar to `call()` but using the spread operator.
	* Cons: May not work as expected in all browsers or situations.

**Library and Special Features**

None of these approaches rely on any specific libraries or features beyond standard JavaScript. However, it's worth noting that older versions of Chrome (before 101) may have different behavior or performance characteristics compared to newer versions.

**Alternatives**

If you were to implement this benchmark yourself, consider the following alternatives:

1. **Arrow functions**: Instead of using `call()` or `apply()`, arrow functions (`(x, ...arr) => Math.max(...arr)`) might provide a more concise and readable solution.
2. **`map()` with `reduce()`**: Using `map()` to create an array and then reducing it with the `Math.max()` function could provide another approach for comparison.

Keep in mind that these alternatives would likely produce similar performance characteristics to the original benchmark, but they might have slightly different execution profiles or syntax.

Related benchmarks:

Math.max - apply, call vs spread ~50K (version: 0)

Compare Math.max(args) using apply, call and spread operator

Comparing performance of: apply vs call and spread vs spread

Created: 4 years ago by: Guest

Jump to the latest result

apply

call and spread

spread

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):