Benchmark: ir1 vs ir2 - MeasureThat.net

Script Preparation code:


var args = (new Int32Array(10000)).map(_ => Math.random(1000000));

Tests:

ir1
[0, 1].concat([].slice.call(args))
ir2
[].concat.apply([], [[0, 1], args]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
ir1
ir2

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 14_4 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0.3 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 14 on iOS 14.4

View result in a separate tab

Test name	Executions per second
ir1	7150.8 Ops/sec
ir2	3553976.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases to understand what's being tested.

**Benchmark Definition**

The benchmark definition is an array of strings, each representing a specific code snippet that will be executed repeatedly in the benchmarking process. In this case, there are two definitions:

1. `[0, 1].concat([].slice.call(args))`
2. `[] .concat.apply([], [[0, 1], args])`

**Options Being Compared**

These two code snippets represent two different approaches to concatenate arrays:

**IR1 (Interpreted)**: The first snippet uses the `concat` method with the spread operator (`[...]`) to concatenate the array `[0, 1]` with the result of calling `slice.call(args)`. This approach is interpreted by the JavaScript engine.

**IR2 (Interpreted, but with native code optimization)**: The second snippet uses the `apply` method and an array literal to achieve a similar result. Although it appears to be using interpreted code, some browsers can optimize this expression into native machine code, which can lead to better performance on certain platforms.

**Pros and Cons**

* **Interpreted (IR1)**
	+ Pros: Easy to read and write, doesn't require special knowledge of browser-specific optimizations.
	+ Cons: May be slower due to interpretation overhead, less optimized for specific platforms or hardware architectures.
* **Optimized IR2**
	+ Pros: Can lead to better performance on certain platforms (e.g., mobile devices), uses native machine code when possible.
	+ Cons: Requires knowledge of browser-specific optimizations and may not work in all environments.

**Library**

In this benchmark, no specific libraries are used. The focus is solely on the JavaScript syntax and interpreter optimization techniques.

**Special JS Feature or Syntax**

There are a few features that might be worth noting:

* **Spread operator (`[...]`)**: Introduced in ECMAScript 2015 (ES6), the spread operator allows for easy creation of arrays from objects, tuples, or other iterable sources. In this case, it's used to expand an array-like object into a new array.
* **Native machine code optimization**: Some browsers can optimize certain expressions into native machine code when possible. This is not specific to any particular library but rather a feature of the JavaScript engine itself.

**Other Alternatives**

If you were to design similar benchmarks, you might consider testing other approaches to array concatenation, such as:

* Using `Array.prototype.push` instead of `concat`
* Creating an intermediate array and then concatenating it with the original arrays
* Using `reduce()` or other aggregation methods

Keep in mind that these alternatives would likely have different performance characteristics depending on the specific use case and target environment.

In summary, this benchmark compares two approaches to concatenate arrays using interpreted JavaScript code, highlighting the pros and cons of each approach and considering alternative techniques.

Related benchmarks:

ir1 vs ir2 (version: 0)

Comparing performance of: ir1 vs ir2

Created: 3 years ago by: Guest

Jump to the latest result

ir1

ir2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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