Benchmark: 1 vs x loop iterators

Script Preparation code:

var k = [1,2,3,4,5,6,7,8,9,10,-11,-12,-13,"aa",,]

Tests:

one iterator
var k = 0; for (; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; };
multi-iterator
for (let k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (let k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (let k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }; for (let k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
one iterator
multi-iterator

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, the options compared, their pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is testing the performance of JavaScript in a specific scenario: calculating the XOR operation on an array element with itself. The benchmark consists of four identical loops that perform this calculation, but with different iterator mechanisms:

1. **Direct indexing**: `k[k] = k[k] ^ k[k];` (single iterator)
2. **Multi-iterator**: `for (let k = 0; k < 100; k++) { k[k] = k[k] ^ k[k]; }` (multiple iterators)

**Options Compared**

The benchmark compares the performance of two different approaches:

1. **Direct indexing with a single iterator**: This approach uses direct indexing to access array elements, which can lead to slower performance due to array bounds checking and potential cache misses.
2. **Multi-iterator**: This approach uses multiple iterations to calculate the same value, potentially exploiting loop unrolling or other optimization techniques.

**Pros and Cons**

1. **Direct indexing with a single iterator**:
	* Pros: Simple implementation, easy to understand.
	* Cons: Potential performance overhead due to array bounds checking and cache misses.
2. **Multi-iterator**:
	* Pros: Potential for better performance through loop unrolling or optimization techniques.
	* Cons: More complex implementation, may be harder to understand.

**Library and Special JS Features**

The benchmark does not explicitly use any libraries. However, it uses the `let` keyword with a declaration block, which is a feature introduced in ECMAScript 2015 (ES6). This syntax allows for block-level scope and can improve code readability.

**Other Considerations**

* The benchmark uses a relatively small array size of 100 elements, which may not be representative of larger datasets.
* The use of `var` instead of `let` or `const` could lead to variable hoisting issues.
* The benchmark does not account for other potential performance factors, such as memory allocation or garbage collection.

**Alternatives**

Other alternatives for measuring JavaScript performance might include:

1. **Math operations**: Measuring the performance of basic arithmetic operations (e.g., addition, multiplication) in a loop.
2. **String manipulation**: Measuring the performance of string concatenation, substring extraction, or other string-related tasks.
3. **DOM manipulations**: Measuring the performance of DOM node creation, removal, or modification.

Keep in mind that these alternatives might not directly relate to the specific scenario tested by the provided benchmark.

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/100.0.4896.127 Safari/537.36 Edg/100.0.1185.50

Browser/OS: Chrome 100 on Windows

View result in a separate tab

Test name	Executions per second
one iterator	5525.5 Ops/sec
multi-iterator	5498.6 Ops/sec

Related benchmarks:

1 vs x loop iterators (version: 0)

Comparing performance of: one iterator vs multi-iterator

Created: 2 years ago by: Guest

Jump to the latest result

one iterator

multi-iterator

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):