Benchmark: Big inside of small vs small inside of big loops

Script Preparation code:

var n = 10;
var m = 10000;

Tests:

big inside of small
for(var i = 0; i < n; i++) { for (var j = 0; j < m; j++) { console.log(i,j) } }
small inside of big
for(var i = 0; i < m; i++) { for (var j = 0; j < n; j++) { console.log(i,j) } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
big inside of small
small inside of big

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark JSON and explain what is being tested, compared, and the pros/cons of each approach.

**Benchmark Definition**

The benchmark defines two test cases:

1. "big inside of small": This test case iterates over `n` (small) times for each `m` (large) iteration.
2. "small inside of big": This test case iterates over `m` (large) times for each `n` (small) iteration.

**Script Preparation Code**

The script preparation code sets up two variables:
```javascript
var n = 10;
var m = 10000;
```
These variables are used in the benchmark definitions to control the number of iterations.

**Html Preparation Code**

There is no HTML preparation code, which means that the benchmark only runs in a headless browser environment (e.g., using the Chrome DevTools or a similar tool).

**Benchmark Comparison**

The two test cases compare the performance of iterating over `n` and `m` in different orders. The idea is to see which approach is faster.

**Pros/Cons of each approach:**

1. **"big inside of small"**:
	* Pros: This approach might be faster because it iterates over the smaller value (`n`) first, reducing the number of iterations.
	* Cons: It's possible that the compiler or optimizer could optimize this code to be just as fast as the other approach.
2. **"small inside of big"**:
	* Pros: This approach might also be faster because it iterates over the larger value (`m`) first, reducing the number of iterations.
	* Cons: Similar to the previous point, the compiler or optimizer might still optimize this code to be just as fast.

In general, these two test cases are comparing different iteration orders, and the winner is likely to depend on the specific browser, JavaScript engine, and compiler optimizations used.

**Library usage**

There is no library explicitly mentioned in the benchmark JSON. However, it's possible that a library like `console.log()` or other built-in functions might be used implicitly.

**Special JS feature/syntax**

None of the test cases use any special JavaScript features or syntax (e.g., async/await, ES6 classes, etc.).

**Alternatives**

If you wanted to create a similar benchmark, you could try using different iteration orders or even compare other micro-optimization techniques. Some ideas:

* Compare the performance of using `for` loops versus `while` loops.
* Use different data structures (e.g., arrays vs. objects) for iteration.
* Experiment with different compiler or optimizer settings (e.g., `-O0`, `-O1`, etc.).

Keep in mind that creating a robust benchmark requires careful consideration of various factors, such as hardware, software, and JavaScript engine specifics.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.119 YaBrowser/22.3.0.2520 Yowser/2.5 Safari/537.36

Browser/OS: Yandex Browser 22 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
big inside of small	1.6 Ops/sec
small inside of big	1.7 Ops/sec

Related benchmarks:

Big inside of small vs small inside of big loops (version: 0)

Comparing performance of: big inside of small vs small inside of big

Created: 5 years ago by: Guest

Jump to the latest result

big inside of small

small inside of big

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):