Benchmark: for [i,j] big/small -_-

Tests:

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

Let's break down the benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is designed to compare the performance of two loop configurations:

1. `for (let i = 0; i < 1000000; i++)` with a nested loop `for (let j = 0; j < 10; j++)`
2. `for (let i = 0; i < 10; i++)` with a nested loop `for (let j = 0; j < 1000000; j++)`

These loops are likely to be used in common JavaScript scenarios, such as iterating over arrays or objects.

**Options Compared**

The two options being compared are:

1. **Outer loop size**: In the first option, the outer loop iterates `1000000` times, while in the second option, it iterates only `10` times.
2. **Inner loop size**: In the first option, the inner loop iterates `10` times, while in the second option, it iterates `1000000` times.

**Pros and Cons**

The choice of loop configuration can significantly impact performance. Here's a brief summary:

* **Outer loop size**: A larger outer loop may lead to more overhead due to function call stack management, but a smaller inner loop may reduce memory allocation and deallocation costs.
* **Inner loop size**: A larger inner loop can take advantage of better cache locality, reducing memory accesses and improving performance. However, a smaller inner loop may reduce the effectiveness of this optimization.

**Other Considerations**

* **Cache locality**: The choice of loop configuration can impact cache locality, which is essential for efficient memory access.
* **Function call overhead**: The number of function calls involved in each loop iteration can affect performance. In this case, the outer loop is likely to incur more function call overhead due to its larger size.

**Library Usage**

In the provided benchmark, no libraries are explicitly mentioned. However, JavaScript engines like V8 (used by Chrome) may use various optimizations and caching mechanisms that could impact the results.

**Special JS Features or Syntax**

None of the test cases utilize special JavaScript features or syntax, such as async/await, Promises, or WebAssembly.

**Alternatives**

Other alternatives for benchmarking loop performance might include:

* Using a different language or framework
* Varying other parameters, such as data type (e.g., integers vs. floats) or distribution (e.g., uniform vs. random)
* Adding additional loops or conditions to simulate real-world scenarios
* Utilizing more advanced profiling tools, such as those provided by JavaScript engines or third-party libraries.

In summary, the benchmark is designed to compare two loop configurations with varying outer and inner loop sizes, providing insight into the performance trade-offs involved in optimizing loop iteration.

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	221.6 Ops/sec
small i, big j	355.2 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):