Benchmark: callbacks: inline function vs global

Tests:

global add
function addGlobal(a, b) { return a + b; } function myFunc(num) { return addGlobal(num, num+1); } for (let i = 0; i < 10000; i++) { myFunc(i); }
inline add
function myFunc(num) { function addInline(a, b) { return a + b; } return addInline(num, num+1); } for (let i = 0; i < 10000; i++) { myFunc(i); }
global add 2
// same as first one, just to ensure benchmarks are valid function addGlobal(a, b) { return a + b; } function myFunc(num) { return addGlobal(num, num+1); } for (let i = 0; i < 10000; i++) { myFunc(i); }
inline add 2
function myFunc(num) { function addInline(a, b) { return a + b; } return addInline(num, num+1); } for (let i = 0; i < 10000; i++) { myFunc(i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
global add
inline add
global add 2
inline add 2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36

Browser/OS: Chrome 128 on Windows

View result in a separate tab

Test name	Executions per second
global add	259962.6 Ops/sec
inline add	194704.4 Ops/sec
global add 2	259526.0 Ops/sec
inline add 2	217225.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

The provided benchmark compares two approaches to implementing an `add` function: one using a global function and another using an inline function. The test cases are designed to measure the performance difference between these two approaches.

**Global Function Approach**

In this approach, the `add` function is defined globally, outside of any scope or function. This means that every time `myFunc` is called, it will use the global `add` function instead of creating a new local copy.

```javascript
function addGlobal(a, b) {
  return a + b;
}

function myFunc(num) {
  return addGlobal(num, num+1);
}
```

**Inline Function Approach**

In this approach, the `add` function is defined inside the `myFunc` function as an inline function. This means that every time `myFunc` is called, it will create a new local copy of the `add` function.

```javascript
function myFunc(num) {
  function addInline(a, b) {
    return a + b;
  }
  return addInline(num, num+1);
}
```

**Comparison**

The main difference between these two approaches is that the global function approach uses a shared instance of the `add` function, while the inline function approach creates a new local copy every time `myFunc` is called.

**Pros and Cons**

* **Global Function Approach:**
	+ Pros:
		- Reduces memory allocation overhead since only one instance of the `add` function is created.
		- Can be faster since there's less overhead associated with function creation and lookup.
	+ Cons:
		- May lead to unexpected behavior if the global function is modified or accessed concurrently.
		- May not work well in certain environments where functions are cached or optimized.
* **Inline Function Approach:**
	+ Pros:
		- Allows for better code organization and reusability since the `add` function is defined within a specific scope.
		- Can be more predictable and easier to debug since every call creates a new instance of the `add` function.
	+ Cons:
		- Increases memory allocation overhead since a new local copy of the `add` function is created every time `myFunc` is called.
		- May lead to slower performance due to the additional overhead associated with function creation and lookup.

**Library and Special Features**

There are no libraries used in this benchmark, but it's worth noting that MeasureThat.net may use some internal libraries or optimizations under the hood.

As for special JavaScript features, none are explicitly mentioned. However, it's worth mentioning that this benchmark is testing a common optimization technique in JavaScript: function caching or memoization. The idea is to optimize performance by reusing existing function instances instead of creating new ones every time they're called.

**Other Alternatives**

If you were to explore alternative approaches to this benchmark, some options might include:

1. Using a caching mechanism to store the results of expensive function calls.
2. Implementing a hybrid approach that combines the benefits of both global and inline functions (e.g., using a shared instance for frequently called functions).
3. Comparing performance with other optimization techniques, such as loop unrolling or compiler optimizations.

Keep in mind that this benchmark is specifically designed to compare two approaches, so exploring alternative options might not be directly relevant to the original question.

Related benchmarks:

callbacks: inline function vs global (version: 0)

Comparing performance of: global add vs inline add vs global add 2 vs inline add 2

Created: one year ago by: Guest

Jump to the latest result

global add

inline add

global add 2

inline add 2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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