Benchmark: pi 180 - MeasureThat.net

Script Preparation code:

const preCalc = Math.PI / 180;

let calculationOneX;
let calculationOneY;

let calculationTwoX;
let calculationTwoY;

Tests:

precalc
calculationOneX = 100 + Math.cos(91 * preCalc) * 40; calculationOneY = 100 + Math.sin(91 * preCalc) * 40;
live
calculationTwoX = 100 + Math.cos(91 * (Math.PI / 180)) * 40; calculationTwoY = 100 + Math.sin(91 * (Math.PI / 180)) * 40;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
precalc
live

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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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.

**What is tested?**

The benchmark measures the performance of two different approaches to calculating trigonometric values for angles in degrees, converted to radians using the `Math.PI / 180` trick. The two approaches are:

1. Pre-calculation: This approach calculates the pre-calculated value (`preCalc`) once before running the benchmark, and then uses this value throughout the calculation.
2. Live calculation: This approach does not pre-calculate the values and instead calculates them on the fly for each iteration of the benchmark.

**Options compared**

The two options are compared in terms of their performance impact on the benchmark. The pre-calculation approach aims to reduce the number of calculations required, while the live calculation approach does not make any assumptions about the frequency of use.

**Pros and cons**

**Pre-calculation approach:**

Pros:

* Reduces the number of calculations required
* Can lead to better performance due to reduced memory allocation and garbage collection overhead

Cons:

* Requires an initial calculation that is done only once, which may not be necessary if the values are never reused.
* May lead to caching issues if the pre-calculated value is stored in a way that prevents it from being updated.

**Live calculation approach:**

Pros:

* Does not require any assumptions about the frequency of use
* Can be beneficial for benchmarks that need to accurately represent real-world usage patterns

Cons:

* Increases the number of calculations required, which can lead to slower performance.
* May lead to higher memory allocation and garbage collection overhead due to the repeated calculations.

**Library and special JS features**

There is no library being used in this benchmark. However, the use of `Math.PI / 180` is a well-known trick for converting degrees to radians in JavaScript. It's a simple and efficient way to perform the conversion without using any external libraries or functions.

**Other considerations**

When writing benchmarks like this one, it's essential to consider the following:

* Ensure that the benchmark accurately represents the behavior you want to measure.
* Use proper normalization techniques to ensure consistent results across different platforms and hardware configurations.
* Consider the use of sampling techniques to reduce the impact of outliers on your results.

**Alternative approaches**

Other alternative approaches could include:

* Using a Just-In-Time (JIT) compiler or a dynamic compilation technique to optimize the calculation.
* Utilizing parallel processing or concurrent execution to take advantage of multi-core processors.
* Implementing a caching mechanism to store pre-calculated values for frequently used angles.
* Using a different data structure, such as a lookup table, to store and retrieve trigonometric values.

By considering these factors and approaches, you can write more accurate and informative benchmarks that provide valuable insights into the performance of your code.

Related benchmarks:

pi 180 (version: 0)

Comparing performance of: precalc vs live

Created: 8 years ago by: Guest

Jump to the latest result

precalc

live

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):