Benchmark: lambdas-noinline-fun-creation3

Script Preparation code:

var x = [];
for(i=0; i<1000; i++){
	x.push(i)
}
function justReturn(item) { return item }
function sideEffect(arr, i) { arr[i] = x[i] }

Tests:

lambda (created each time)
let a1 = []; function justReturn2(item) { return item } for(i=0; i<1000; i++){ a1[i] = justReturn2(x[i]) }
lambda (created once)
let a2 = []; for(i=0; i<1000; i++){ a2[i] = justReturn(x[i]) }
lambda (side-effect inline)
let a3 = []; function sideEffect2(i) { a3[i] = x[i] } for(i=0; i<1000; i++){ sideEffect2(i) }
lambda (side-effect)
let a4 = []; for(i=0; i<1000; i++){ sideEffect(a4, i) }
no lambda
let a5 = []; for(i=0; i<1000; i++){ a5[i] = x[i] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lambda (created each time)
lambda (created once)
lambda (side-effect inline)
lambda (side-effect)
no lambda

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Definition**

The provided JSON defines a benchmark named "lambdas-noinline-fun-creation3". This benchmark measures the performance of different JavaScript code snippets that create and use lambda functions (anonymous functions) to transform an array `x`.

**Options Compared**

There are five test cases compared:

1. **`lambda (created each time)`**: Creates a new lambda function on every iteration.
2. **`lambda (created once)`**: Creates a single lambda function before the loop starts.
3. **`lambda (side-effect inline)`**: Moves the side-effect of updating `a3` inside the loop, eliminating the need for an explicit assignment outside the loop.
4. **`lambda (side-effect)`**: Uses the `sideEffect` function to update `a4` on every iteration.
5. **`no lambda`**: Does not use any lambda functions.

**Pros and Cons of Each Approach**

1. **`lambda (created each time)`**:
	* Pros: Easy to understand, minimizes memory usage.
	* Cons: Creates a new function object on every iteration, which can lead to slower performance due to function overhead.
2. **`lambda (created once)`**:
	* Pros: Can be faster than the previous option since it only creates one function object.
	* Cons: Requires an initial memory allocation for the single function object.
3. **`lambda (side-effect inline)`**:
	* Pros: Eliminates the need for explicit assignment outside the loop, reduces memory usage.
	* Cons: May not be immediately clear to readers due to its unconventional syntax.
4. **`lambda (side-effect)`**:
	* Pros: Easy to understand and maintain, avoids explicit assignment outside the loop.
	* Cons: Requires a separate function call for side-effect, which may incur additional overhead.
5. **`no lambda`**:
	* Pros: Avoids unnecessary function creation and memory usage.
	* Cons: May not be as efficient as using lambdas, especially for array transformations.

**Library Usage**

The `justReturn` and `sideEffect` functions are used in the benchmark. These functions appear to be part of a custom library or a set of utility functions designed for this specific benchmark.

* **`justReturn`**: A simple function that takes an input and returns it unchanged.
* **`sideEffect`**: A function that updates a given array with values from another array.

**Special JS Features/Syntax**

The benchmark does not explicitly use any special JavaScript features or syntax beyond standard ES5/ES6 language constructs. However, the `let` keyword is used in all test cases, which is a modern JavaScript feature introduced in ECMAScript 2015 (ES6).

**Alternatives**

Other alternatives for measuring performance differences between these code snippets could include:

* Using a different programming language or paradigm (e.g., Python, C++).
* Employing alternative optimization techniques, such as loop unrolling or fusion.
* Implementing a more sophisticated caching mechanism to reduce function creation overhead.

Keep in mind that the choice of benchmark and test cases ultimately depends on the specific use case and requirements.

Related benchmarks:

lambdas-noinline-fun-creation3 (version: 0)

Comparing performance of: lambda (created each time) vs lambda (created once) vs lambda (side-effect inline) vs lambda (side-effect) vs no lambda

Created: 5 years ago by: Guest

Jump to the latest result

lambda (created each time)

lambda (created once)

lambda (side-effect inline)

lambda (side-effect)

no lambda

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):