Benchmark: same test - MeasureThat.net

Tests:

fn 1
const ka = ['id', 'token', 'family', 'grade', 'hex']; const k = 'token'; const fl = function(e) { return e !== k }; const fna = function() { return ka.filter(fl) }; fna();
fn 2
const ka = ['id', 'token', 'family', 'grade', 'hex']; const k = 'token'; const fl = function(e) { return e !== k }; const fna = function() { return ka.filter(fl) }; fna();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
fn 1
fn 2

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.

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

**Benchmark Definition JSON**

The provided Benchmark Definition JSON is empty, indicating that no specific optimizations or variations are defined for the benchmark. This means that the test case will be executed without any modifications to the code.

**Individual Test Cases**

There are two identical test cases:

1. `fn 1`
2. `fn 2`

Each test case consists of a JavaScript function that filters an array using a custom filter function (`fl`).

The relevant parts of the benchmark definition are:
```javascript
const ka = ['id', 'token', 'family', 'grade', 'hex'];
const k = 'token';
const fl = function(e) {
  return e !== k;
};
const fna = function() {
  return ka.filter(fl);
};
fna();
```
**Filter Function (`fl`)**

The `fl` function is a simple custom filter function that takes an element `e` as input and returns `true` if `e` is not equal to the string `'token'`. This filter function will be used to filter the array `ka`.

**Array Filtering (`fna`)**

The `fna` function uses the `filter()` method to apply the custom filter function (`fl`) to the array `ka`. The resulting filtered array is then returned.

**Options Compared**

In this benchmark, two options are compared:

1. **Default implementation**: The original code with no modifications.
2. **Custom filtering**: The modified code that uses a custom filter function (`fl`).

The comparison will likely measure the performance difference between these two approaches.

**Pros and Cons of Each Approach**

1. **Default Implementation (Original Code)**
	* Pros:
		+ Less overhead due to custom filtering.
		+ Potential for better cache locality.
	* Cons:
		+ May not be optimized for specific use cases.
2. **Custom Filtering (Modified Code)**
	* Pros:
		+ Can be optimized for specific use cases, such as performance-critical filters.
		+ Provides more flexibility in terms of filtering logic.
	* Cons:
		+ Adds overhead due to the custom filter function and array filtering.
		+ May lead to slower execution times.

**Other Considerations**

* The benchmark may also consider other factors, such as the size of the input array (`ka`) or the number of executions per second (ExecutionsPerSecond).
* If the test case uses a specific JavaScript library, it may be worth noting which library is being used and its purpose.

**Alternative Approaches**

Some alternative approaches that could be considered for this benchmark include:

1. **Using built-in filtering methods**: Instead of using a custom filter function, the code could use built-in filtering methods like `Array.prototype.filter()` or `Array.prototype.every()`.
2. **Using caching mechanisms**: To reduce overhead due to repeated array filtering, the code could implement caching mechanisms to store filtered arrays.
3. **Using parallel processing**: If the input size is large and performance-critical, the benchmark could explore using parallel processing techniques to accelerate execution.

Please note that these alternative approaches may not be relevant depending on the specific requirements of the test case and the desired outcome of the benchmark.

Related benchmarks:

same test (version: 0)

Comparing performance of: fn 1 vs fn 2

Created: 2 years ago by: Guest

Jump to the latest result

fn 1

fn 2

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):