Benchmark: recurse pyramid - MeasureThat.net

Script Preparation code:

function getPydata(depth,start = 0){
  if(depth <= 1) return new Array([start+1]);
  var a= [];
  var i = 0;
  do {
    i++;
    a.push(++start);
  } while (i < depth);
  return [a].concat(getPydata((depth-1),a[a.length-1]));
}

Tests:

test1
console.log(getPydata(5));
test2
console.log(getPydata(100));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
test1
test2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition, test cases, and latest benchmark result to explain what's being tested and the pros/cons of different approaches.

**Benchmark Definition**

The `getPydata` function is defined in the `Script Preparation Code` section. It creates a recursive pyramid data structure by pushing numbers from 1 to `depth` into an array. The function then recursively calls itself with the same depth, but starts from the last element of the previous array.

**Options Compared**

In this benchmark, two options are being compared:

1. **Recurse Pyramid Approach**: This is the original implementation of `getPydata`, which creates a recursive pyramid data structure.
2. **Iterative Approach**: Not explicitly mentioned in the provided code, but we can infer that an iterative approach would be used instead of recursion.

**Pros and Cons**

**Recurse Pyramid Approach:**

Pros:

* Easy to implement and understand
* Can be parallelized easily using multiple threads or processes

Cons:

* May lead to stack overflow errors for large inputs due to recursive calls
* May have performance overhead due to function call overhead

**Iterative Approach:**

Pros:

* Avoids stack overflow errors and potential performance overhead due to function calls
* Can be more efficient for large inputs

Cons:

* More complex implementation and harder to understand
* May require additional memory for the iteration variables

**Library**

In this benchmark, no specific library is being used. However, if we consider a broader context, we can see that `getPydata` uses an iterative approach under the hood (not explicitly mentioned in the provided code).

**Special JS Feature or Syntax**

No special JavaScript feature or syntax is used in this benchmark.

**Alternative Approaches**

Other approaches to measure the performance of the `getPydata` function could include:

1. **Memoization**: Store the results of previous computations to avoid redundant calculations.
2. **Caching**: Store the intermediate results of the recursive calls to reduce the number of computations.
3. **Parallelization**: Use multiple threads or processes to execute the same calculation in parallel.

**Other Considerations**

When designing a benchmark, it's essential to consider factors such as:

* **Input size**: How does the performance change with varying input sizes?
* **Warm-up time**: Does the system need time to warm up before taking measurements?
* **Randomization**: Should the inputs be randomly generated or fixed for consistency?

In this case, the benchmark measures the execution speed of the `getPydata` function for two different input values (5 and 100). The results can help identify performance bottlenecks or areas where optimization is needed.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36

Browser/OS: Chrome 75 on Linux

View result in a separate tab

Test name	Executions per second
test1	19256.7 Ops/sec
test2	460.9 Ops/sec

Related benchmarks:

recurse pyramid (version: 0)

Comparing performance of: test1 vs test2

Created: 5 years ago by: Guest

Jump to the latest result

test1

test2

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):