Benchmark: Lerp Testing - MeasureThat.net

Tests:

Lerp 1
lerp(10, 0, 1); function lerp(s,d,t){return s+t*(d-s)}
Lerp 2
lerp(10, 0, 1); function lerp(s,d,t){t=t<0?0:t;t=t>1?1:t;return s+(d-s)*t;}
Lerp 3
lerp(10, 0, 1); function lerp(s,d,t){return s*(1-t)+d*t}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lerp 1
Lerp 2
Lerp 3

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/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Windows

View result in a separate tab

Test name	Executions per second
Lerp 1	1357608064.0 Ops/sec
Lerp 2	1324467456.0 Ops/sec
Lerp 3	1329749248.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**What is being tested?**

The benchmark measures the performance of different linear interpolation (lerp) functions in JavaScript. Linear interpolation is a technique used to calculate the value between two points, often used in graphics and animations.

**Options compared:**

There are three lerp functions being compared:

1. **Simple linear interpolation**: `function lerp(s,d,t){return s+t*(d-s)}`
	* This function uses a simple formula for linear interpolation.
2. **Clamped linear interpolation**: `function lerp(s,d,t){t=t<0?0:t;t=t>1?1:t;return s+(d-s)*t}`
	* This function clamps the interpolated value between 0 and 1, preventing it from going outside this range.
3. **Simplified linear interpolation**: `function lerp(s,d,t){return s*(1-t)+d*t}`
	* This function uses a different formula for linear interpolation, which is more efficient than the first two options.

**Pros and cons of each approach:**

1. **Simple linear interpolation**:
	* Pros: Easy to understand and implement.
	* Cons: May not be as efficient or accurate as other approaches.
2. **Clamped linear interpolation**:
	* Pros: Prevents interpolated values from going outside the 0-1 range, ensuring a stable output.
	* Cons: May add unnecessary complexity and overhead.
3. **Simplified linear interpolation**:
	* Pros: More efficient than the first two options and may be more accurate due to reduced rounding errors.
	* Cons: Requires careful consideration of the formula to ensure correct results.

**Library usage:**

There is no explicit library mentioned in the benchmark definition or test cases. However, some libraries like Lodash (https://lodash.com/) provide built-in functions for linear interpolation that can be used instead of implementing their own.

**Special JS features or syntax:**

None are explicitly mentioned in this benchmark. However, it's worth noting that JavaScript features like async/await, Promises, and Web Workers may not be relevant to this specific microbenchmark.

**Other alternatives:**

If you wanted to create a similar benchmark for other mathematical functions or algorithms, consider the following options:

1. **Math.js**: A lightweight math library (https://mathjs.org/) that provides implementations for various mathematical functions.
2. **Faster-for-a-Loop** (https://github.com/gregoryp/faster-for-a-loop): A tool designed to optimize JavaScript loops by detecting and applying optimizations, including linear interpolation.
3. **Benchmarking libraries**: Consider using specialized benchmarking libraries like Benchmark.js (https://www.benchmarkjs.com/) or JSPerf (http://jsperf.com/), which provide features for measuring performance and comparing different implementations.

Keep in mind that this is not an exhaustive list, and there may be other alternatives depending on your specific needs and requirements.

Related benchmarks:

Lerp Testing (version: 0)

Testing different lerp implementations

Comparing performance of: Lerp 1 vs Lerp 2 vs Lerp 3

Created: one year ago by: Guest

Jump to the latest result

Lerp 1

Lerp 2

Lerp 3

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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