Benchmark: for [i,j] big/small ;

Tests:

big i, small j
let cnt = 0; for (let i = 0; i < 1000000; i++) { for (let j = 0; j < 10; j++) { cnt++; } } console.log(cnt);
small i, big j
let cnt = 0; for (let i = 0; i < 10; i++) { for (let j = 0; j < 1000000; j++) { cnt++; } } console.log(cnt);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
big i, small j
small i, big j

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 data and explain what's being tested.

**Benchmark Definition:**

The provided `Benchmark Definition` json represents two different microbenchmarks:

1. `for [i,j] big/small ;`: This is a generic for loop with two variables, `i` and `j`, where `big` means large values and `small` means small values.
2. The actual loops defined in the `Test Case`s: Two test cases are provided, each defining a specific nested for loop structure:
	* `for (let i = 0; i < 1000000; i++) { for (let j = 0; j < 10; j++) { ... } }`
	* `for (let i = 0; i < 10; i++) { for (let j = 0; j < 1000000; j++) { ... } }`

**Options Compared:**

In this benchmark, two options are being compared:

1. **Large inner loop vs Small inner loop**: The first test case uses a large inner loop (`j < 1000000`), while the second test case uses a small inner loop (`j < 10`). This comparison tests the performance impact of varying inner loop sizes on the overall execution time.
2. **Large outer loop vs Small outer loop**: Both test cases have a relatively large outer loop (`i < 1000000` or `i < 10`). However, this is not being directly compared in the benchmark.

**Pros and Cons:**

1. **Large inner loop:**
	* Pros:
		+ Can be beneficial for certain algorithms where large amounts of work are done in the inner loop.
	* Cons:
		+ May lead to increased memory usage and slower performance due to the larger number of iterations.
2. **Small inner loop:**
	* Pros:
		+ Reduces memory usage and can potentially improve performance by reducing the number of iterations.
	* Cons:
		+ May not be suitable for algorithms that rely heavily on the inner loop.

**Library and Special JS Features:**

The provided benchmark does not explicitly mention any libraries or special JavaScript features. However, it's worth noting that modern JavaScript engines often have various optimization techniques and optimizations built-in, such as:

* Just-In-Time (JIT) compilation
* Ahead-Of-Time (AOT) compilation
* Garbage collection

**Other Alternatives:**

To test similar benchmarks, you could consider the following alternatives:

1. **Google Benchmark**: A high-performance benchmarking framework for C++ and JavaScript.
2. **Benchmark.js**: A lightweight JavaScript library for creating microbenchmarks.
3. **jsperf**: A classic benchmarking tool for measuring JavaScript performance.

Keep in mind that each alternative has its own strengths and weaknesses, and the choice of which one to use depends on your specific needs and goals.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.81 Safari/537.36

Browser/OS: Chrome 98 on Windows

View result in a separate tab

Test name	Executions per second
big i, small j	114.3 Ops/sec
small i, big j	182.8 Ops/sec

Related benchmarks:

for [i,j] big/small ; (version: 0)

Comparing performance of: big i, small j vs small i, big j

Created: 2 years ago by: Guest

Jump to the latest result

big i, small j

small i, big j

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):