Benchmark: Tween optimization

Script Preparation code:

function tween1(f, easing) {
	easing /= 100;
	easing += 1;
	easing /= 2;
	easing *= 0.6666;
	const q = 1 - f;
	return 3 * Math.pow(q, 2) * f * easing + 3 * q * Math.pow(f, 2) * (0.3334 + easing) + Math.pow(f, 3);
};

function tween2(f, easing) {
	const q = 1 - f;
	const f2 = f * f;
	easing = easing * 0.01 + 1;
	return q * q * f * easing + q * f2 * (1 + easing) + f2 * f;
};

var sum1 = 0;
var sum2 = 0;
function test1() { sum1 += tween1(Math.random(), Math.random() * 200 - 100); };
function test2() { sum2 += tween2(Math.random(), Math.random() * 200 - 100); };

Tests:

tween1
test1();
tween2
test2();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
tween1
tween2

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'll break down the provided JSON and explain what's tested, compared options, pros and cons, and other considerations.

**Benchmark Definition**

The benchmark measures the performance of two tweening functions, `tween1` and `tween2`, which are used to calculate a value based on the input `f` and an easing function. The easing function is a mathematical function that affects how the calculated value changes over time.

**Comparison Options**

The benchmark compares the performance of two different approaches:

1. **`tween1`**: This function uses a complex formula with multiple operations, including exponentiation, multiplication, and addition. It also has a few arithmetic errors (e.g., `teasing /= 100;` should be `easing /= 100;`).
2. **`tween2`**: This function is significantly simpler than `tween1`. It uses a single operation with an exponentiation and a multiplication, but it's also incorrect in some places (e.g., `const f2 = f * f;` should be `const f2 = Math.pow(f, 2);`).

**Pros and Cons of Each Approach**

* **`tween1`**:
	+ Pros: None notable
	+ Cons:
		- Complex formula with multiple operations, which can lead to performance issues.
		- Arithmetic errors, which can affect accuracy.
		- May be harder to optimize due to its complexity.
* **`tween2`**:
	+ Pros:
		- Simpler and more straightforward formula, making it easier to understand and optimize.
		- Fewer operations, which can result in better performance.
	+ Cons:
		- Incorrect calculations, which can lead to inaccurate results.

**Library Usage**

There is no explicit library usage in the benchmark definition. However, if we look at the `test1` and `test2` functions, they use a `sum` variable to accumulate the results of the tweening function calls. This suggests that the benchmark is using a simple array or list data structure.

**Special JS Features/Syntax**

There are no explicit mentions of special JavaScript features or syntax in the benchmark definition. However, if we look at the `tween1` and `tween2` functions, they use a non-standard way to calculate the easing value (`easing /= 100;`). This is not a commonly used feature in JavaScript.

**Other Alternatives**

To optimize the performance of these tweening functions, alternative approaches could be considered:

* **Just-In-Time (JIT) compilation**: The functions could be compiled to machine code at runtime using a JIT compiler like V8.
* **Simplify and optimize the formulas**: The formulas used in `tween1` and `tween2` could be simplified and optimized for better performance.
* **Use a different data structure**: Instead of using an array or list, the benchmark could use a more efficient data structure like a queue or a heap.

Overall, the benchmark is designed to measure the performance difference between two simple tweening functions. The results can help identify areas for optimization and improvement in these functions.

Related benchmarks:

Tween optimization (version: 0)

Comparing performance of: tween1 vs tween2

Created: 4 years ago by: Guest

Jump to the latest result

tween1

tween2

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):