Benchmark: find and ceil - MeasureThat.net

Script Preparation code:

function findAndCeil(d, s) {
  return [...Array(100000).keys()].map(function(v) {
    return v * s 
  }).find(function(v) {
    if(v > d) {
      return v
    } 
  })
}

Tests:

t1
findAndCeil(150, 23)
t2
findAndCeil(89003, 3)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

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Test case name	Result
t1
t2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'd be happy to help you understand what's being tested in the provided JSON benchmark.

**Benchmark Overview**

The benchmark is designed to test the performance of JavaScript engines in finding and returning the ceiling value (the smallest integer greater than or equal to a given number) for two different inputs: `150` and `89003`, with scaling factors of `23` and `3`, respectively. The script preparation code defines a function `findAndCeil` that takes two arguments, `d` and `s`, and returns the ceiling value.

**Options Compared**

The benchmark is comparing the performance of different JavaScript engines (or versions) in executing the same code with slightly varying inputs. The options being compared are:

* Different scaling factors (`23` vs `3`)
* Input values (`150` vs `89003`)

**Pros and Cons of Different Approaches**

There isn't a specific approach being compared, but we can analyze the general trade-offs involved in optimizing JavaScript performance:

1. **Scaling factor optimization**: Optimizing for different scaling factors requires understanding how the engine handles large numbers and identifying bottlenecks that scale poorly with input size.
2. **Input value optimization**: Optimizing for specific input values (e.g., `150` vs `89003`) can help identify issues with handling large or small numbers.

Pros of optimizing for one approach over another:

* Focusing on scaling factors might lead to more robust and efficient engine performance, as it accounts for a broader range of use cases.
* Optimizing for specific input values might provide better performance in those specific scenarios, but may not generalize well across other inputs.

Cons of optimizing for one approach over another:

* Overemphasizing scaling factors might lead to neglecting optimizations for smaller or more common input values.
* Focusing too much on specific input values might result in inefficient code that doesn't scale well with larger inputs.

**Library Usage**

The benchmark script uses the `Array` and `map` functions, which are built-in JavaScript libraries. The `find` function is also used, which is a method of the Array prototype.

No external libraries or dependencies are mentioned in the provided code.

**Special JS Features or Syntax**

There isn't any special JavaScript feature or syntax being used beyond the standard built-ins (e.g., `Array`, `map`, `find`). However, it's worth noting that some engines might optimize for certain features like strict mode or performance-related features.

**Other Alternatives**

Alternative approaches to benchmarking JavaScript performance include:

1. **Benchmarking frameworks**: Libraries like Benchmark.js, jsPerf, or Microbenchmark provide more structured and flexible ways to write benchmarks.
2. **Test-driven development (TDD)**: Writing unit tests for specific code paths can help identify performance bottlenecks and optimize engine behavior.
3. **Profile-guided optimization**: Analyzing execution profiles to identify hotspots in the code can inform optimizations and improve overall engine performance.

Keep in mind that these alternatives might be more suited for larger, more complex projects or enterprise environments where benchmarking is a critical aspect of development.

Related benchmarks:

find and ceil (version: 0)

Comparing performance of: t1 vs t2

Created: 3 years ago by: Guest

Jump to the latest result

t1

t2

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):