Benchmark: new functions - MeasureThat.net

Script Preparation code:

var x = (new Array(100)).fill(0);
function f1() {
  return x.map(function(x) {
    return x + 1;
  });
}
function f2() {
  return x.map(g);
}

function g(x) {
  return x + 1;
}

Tests:

inline
f1();
cached
f2();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
inline
cached

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided JSON data and explain what is being tested on MeasureThat.net.

**Benchmark Definition**

The benchmark definition represents a JavaScript function that performs an operation on an array. The specific function `f1` and `g` are defined within this benchmark:

```javascript
function f1() {
  return x.map(function(x) {
    return x + 1;
  });
}

function g(x) {
  return x + 1;
}
```

Here, we have an array `x` filled with zeros. Two functions, `f1` and `g`, are defined to perform a similar operation: mapping the elements of the array to add 1 to each element.

**Options Compared**

The two main options being compared in this benchmark are:

1. **Inline Function**: The `f1()` function is called inline within the benchmark definition, i.e., `f1();`.
2. **Cached Function**: The `g` function is stored in a variable and then called using its reference (`f2()`) within the benchmark definition.

**Pros and Cons of Each Approach**

1. **Inline Function**
	* Pros:
		+ More efficient since it avoids creating an extra function call.
		+ May have better caching opportunities (less chance of caching being missed).
	* Cons:
		+ Harder to reason about and debug, as the code is tightly coupled with the benchmark definition.
2. **Cached Function**
	* Pros:
		+ Easier to maintain and update, as changes are isolated from the benchmark definition.
		+ May be easier to optimize using caching strategies (e.g., memoization).
	* Cons:
		+ Slower due to the extra function call.

**Library and Special JS Features**

No libraries or special JavaScript features are mentioned in this benchmark. The code is self-contained within the benchmark definition.

**Other Alternatives**

For those who might be interested, alternative approaches to comparing functions could include:

1. **Virtual Functions**: Instead of defining real functions (`f1` and `g`), a virtual function could be created using a function expression that simply returns a value without performing any actual computation.
2. **Lambda Expressions**: Using lambda expressions (e.g., `() => x.map(x => x + 1)` for both `f1` and `g`) could provide another alternative way to compare the functions.

Overall, the MeasureThat.net benchmark provides a simple yet informative example of how different function calling strategies can impact performance.

Latest run results:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36

Browser/OS: Chrome 53 on Mac OS X 10.11.5

View result in a separate tab

Test name	Executions per second
inline	99760.0 Ops/sec
cached	98351.3 Ops/sec

Related benchmarks:

new functions (version: 0)

Comparing performance of: inline vs cached

Created: 8 years ago by: Registered User

Jump to the latest result

inline

cached

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):