Benchmark: loopstest - MeasureThat.net

Tests:

O(N)
const n = 1000; let a = 1; for(let i=0; i<n; i++) { a += i; }
O(N**2)
const n = 1000; let a = 1; for(let i=0; i<n; i++) { for (let j=0; j<n; j++) { a += j; } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
O(N)
O(N**2)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark definition and test cases to explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark named "loopstest". This benchmark measures the performance of two different loops: one with linear time complexity (O(N)) and another with quadratic time complexity (O(N^2)).

The script preparation code is empty, which means that the test case code is isolated and not affected by external factors. The HTML preparation code is also empty, suggesting that no HTML-specific considerations are being tested.

**Test Cases**

There are two individual test cases:

1. **O(N)**
This test case measures the performance of a simple loop that increments a variable `a` by multiplying it with another variable `i`, which ranges from 0 to `n`. This loop has linear time complexity, making it more efficient as the input size (`n`) increases.
2. **O(N^2)**
This test case measures the performance of an nested loop that increments `a` by multiplying it with both `i` and `j`, which ranges from 0 to `n`. This loop has quadratic time complexity, making it less efficient as the input size (`n`) increases.

**Options Compared**

The two options being compared are:

* **Linear Time Complexity (O(N))**: More efficient as the input size (`n`) increases.
* **Quadratic Time Complexity (O(N^2))**: Less efficient as the input size (`n`) increases.

**Pros and Cons of Different Approaches**

1. **Linear Time Complexity (O(N))**:
	* Pros: More efficient as the input size (`n`) increases, reducing overall execution time.
	* Cons: May not be suitable for certain scenarios where quadratic complexity is necessary (e.g., computational intensity).
2. **Quadratic Time Complexity (O(N^2))**:
	* Pros: Suitable for scenarios with high computational intensity, such as nested loops or matrix operations.
	* Cons: Becomes less efficient as the input size (`n`) increases, resulting in slower execution times.

**Library and Purpose**

There is no specific library mentioned in the benchmark definition. However, some modern JavaScript engines (e.g., V8) use various optimizations to improve performance for different types of loops. For example:

* **Loop unrolling**: This technique can reduce the number of iterations required by the loop, improving performance.
* **Cache-aware optimization**: Optimizations that take into account cache behavior, such as minimizing cache misses.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. The test cases use basic JavaScript syntax for loops and variable declarations.

**Alternatives**

If the goal of the microbenchmark is to compare loop performance, other alternatives could include:

* **Microbenchmarks with different languages**: Comparing the performance of a linear time complexity loop implemented in multiple languages (e.g., Python, Java, C++) to identify language-specific optimizations.
* **Benchmarking different compiler or interpreter options**: Evaluating the impact of compiler or interpreter settings on the performance of specific loops or algorithms.
* **Measuring performance under varying input sizes**: Testing the scalability of a linear time complexity loop by varying the input size (`n`) and measuring its impact on execution time.

In summary, this microbenchmark compares the performance of two different loops with linear and quadratic time complexities to evaluate their efficiency and scalability.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36

Browser/OS: Chrome 92 on Windows

View result in a separate tab

Test name	Executions per second
O(N)	1403529.9 Ops/sec
O(N**2)	1370.6 Ops/sec

Related benchmarks:

loopstest (version: 0)

Comparing performance of: O(N) vs O(N**2)

Created: 3 years ago by: Guest

Jump to the latest result

O(N)

O(N**2)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):