Benchmark: Anonymous Function in Loop

Script Preparation code:

function doSomething(x) {
  return true;
}

Tests:

A
for(var i=0; i < 10; i++) { (function(someValue) { return true; })(i); }
B
for(var i=0; i < 10; i++) { doSomething(i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
A
B

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.87 Safari/537.36

Browser/OS: Chrome 78 on Linux

View result in a separate tab

Test name	Executions per second
A	152746864.0 Ops/sec
B	1445376.4 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its test cases.

**Benchmark Description**

The benchmark measures the performance of two different approaches to create an anonymous function within a loop:

1. **Approach A**: Creates a new, self-invoking anonymous function for each iteration of the loop using the `function(someValue) { ... }` syntax.
2. **Approach B**: Calls an existing function `doSomething(x)` for each iteration of the loop.

**Options Compared**

The benchmark compares two options:

1. **Creating a new function on the fly** (Approach A): This approach involves creating a new, self-invoking anonymous function for each iteration of the loop. The benefits include:
	* Each iteration has its own scope and can be optimized independently.
	* Memory allocation is done on the fly, which might be more efficient for small iterations.
However, this approach also has some drawbacks:
	* Creating a new function for each iteration can lead to increased memory allocation and garbage collection overhead.
	* The overhead of creating a new function might outweigh the benefits for large iterations.
2. **Calling an existing function** (Approach B): This approach involves calling an existing function `doSomething(x)` for each iteration of the loop. The benefits include:
	* No memory allocation or function creation is required, which can reduce overhead.
	* Existing optimizations and caching might be applied to the `doSomething` function.
However, this approach also has some drawbacks:
	* Each iteration uses the same scope and may not be optimized independently.
	* Memory allocation for local variables in the loop might still occur.

**Pros and Cons of Different Approaches**

| Approach | Pros | Cons |
| --- | --- | --- |
| Creating a new function on the fly (Approach A) | Each iteration has its own scope, memory allocation is done on the fly. | Increased memory allocation and garbage collection overhead, might outweigh benefits for large iterations. |
| Calling an existing function (Approach B) | No memory allocation or function creation required, existing optimizations may be applied. | Each iteration uses the same scope, might not be optimized independently. |

**Library Usage**

In this benchmark, a library is used to create the `doSomething` function:

```javascript
function doSomething(x) { return true; }
```

The `doSomething` function is an existing function that returns `true`. The purpose of using this function is to demonstrate how calling an existing function can be used as an optimization approach.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax mentioned in the benchmark. However, some advanced techniques like scope closure and lexical scoping might be implied by the use of self-invoking anonymous functions (Approach A).

**Other Alternatives**

Some other alternatives to compare performance include:

* Using `let` or `const` instead of `var` for function declarations
* Using arrow functions (`() => { ... }`) instead of traditional function expressions
* Using a loop with a more efficient iteration method, such as `for (const i of anArray) { ... }`
* Using a different optimization technique, such as memoization or caching

Keep in mind that the choice of alternative approaches depends on the specific use case and performance requirements.

Related benchmarks:

Anonymous Function in Loop (version: 0)

Comparing performance of: A vs B

Created: 7 years ago by: Guest

Jump to the latest result

A

B

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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