Benchmark: Effect of function calls in loops2

Script Preparation code:

var N = 10000;
var xs = Array.from(Array(N).keys())
var ys = xs.map((x) => (x + 3) * 0.89 - 16)

function normalFunction(a, b) {
    return Math.sqrt(a ** 2 + b ** 2)
}

var arrowFunction = (a, b) => Math.sqrt(a ** 2 + b ** 2);

Tests:

No function
var res = new Array(N) for (let i = 0; i < N; i++) { res[i] = Math.sqrt(xs[i]**2 + ys[i]**2) }
With normal function
var res = new Array(N) for (let i = 0; i < N; i++) { res[i] = normalFunction(xs[i], ys[i]) }
With arrow function
var res = new Array(N) for (let i = 0; i < N; i++) { res[i] = arrowFunction(xs[i], ys[i]) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
No function
With normal function
With arrow function

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The provided JSON defines a benchmark that compares the performance of three different approaches for calculating the Euclidean distance between two vectors:

1. Using an arrow function (=>)
2. Using a traditional JavaScript function (`normalFunction`)
3. Without using any functions (i.e., inline calculation)

**Script Preparation Code**

The script preparation code generates two arrays, `xs` and `ys`, with 10,000 elements each. It then defines the three approaches:

1. Normal function: `normalFunction(a, b) { return Math.sqrt(a ** 2 + b ** 2); }`
2. Arrow function: `(a, b) => Math.sqrt(a ** 2 + b ** 2)`
3. Inline calculation: No function is defined for this approach.

**Test Cases**

The benchmark defines three test cases:

1. "No function": Uses the inline calculation approach.
2. "With normal function": Uses the `normalFunction` approach.
3. "With arrow function": Uses the arrow function approach.

**Library and Features Used**

None of the provided code uses any external libraries or special JavaScript features that would require additional explanation. The benchmark only focuses on comparing the performance of three different approaches for calculating the Euclidean distance.

**Pros and Cons of Each Approach**

Here's a brief summary:

1. **No function (inline calculation)**:
	* Pros: Simple, no overhead from creating functions.
	* Cons: Less readable and maintainable code.
2. **With normal function (`normalFunction`)**:
	* Pros: More readable and maintainable code, can be reused in other contexts.
	* Cons: Creates a new function object, which may incur some overhead.
3. **With arrow function**:
	* Pros: Similar to the normal function approach, with the added benefit of being more concise and expressive.
	* Cons: May still incur some overhead due to creating an anonymous function.

**Alternative Approaches**

Other approaches that could be compared in this benchmark include:

1. Using a library like `lodash` or `ramda`, which provides optimized implementations for mathematical functions, including Euclidean distance calculations.
2. Using SIMD (Single Instruction, Multiple Data) instructions, which can significantly accelerate certain numerical computations.
3. Compiling the JavaScript code to native machine code using tools like V8 or WebAssembly.

However, these alternatives would likely require significant changes to the benchmark's script preparation and test cases, and may not be relevant for a simple comparison of function approaches.

Related benchmarks:

Effect of function calls in loops2 (version: 0)

Comparing performance of: No function vs With normal function vs With arrow function

Created: 2 years ago by: Guest

Jump to the latest result

No function

With normal function

With arrow function

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):