Benchmark: lodash xor perf copy

Script Preparation code:

function dec2hex (dec) {
  return dec < 10
    ? '0' + String(dec)
    : dec.toString(16)
}

function generateId (len) {
  var arr = new Uint8Array((len || 40) / 2)
  window.crypto.getRandomValues(arr)
  return Array.from(arr, dec2hex).join('')
}

var a = []
var b = []
for( let i = 0; i < 1000; i++)
{
  a[i] = generateId(16)
  b[i] = generateId(16)
}

Tests:

lodash xor
const res = _.xor(a, b)
with xor function
function systemicDifference(arr1, arr2){ const uniqueValues = new Set([...arr1, ...arr2]); return Array.from(uniqueValues).filter((value) => !arr1.includes(value) || !arr2.includes(value)); } const res = systemicDifference(a, b)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash xor
with xor function

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
lodash xor	3153.6 Ops/sec
with xor function	182.0 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 and explain what's being tested, compared, and other considerations.

**Benchmark Definition**

The benchmark is defined by two separate JSON objects:

1. **Script Preparation Code**: This section defines two functions: `dec2hex` and `generateId`. The `generateId` function generates a random ID in hexadecimal format using the Web Crypto API.
	* `dec2hex`: converts a decimal number to a hexadecimal string.
	* `generateId`: generates a random ID by generating an array of 8-bit values ( Uint8Array ) using the Web Crypto API, then converting each value to a hexadecimal string using `dec2hex`, and joining them together.

The script preparation code creates two arrays: `a` and `b`, both with 1000 elements. Each element in the arrays is generated by calling `generateId(16)`, which produces a random ID for each element.

2. **Html Preparation Code**: This section is empty (`null`) and does not appear to be used in this benchmark.

**Individual Test Cases**

The benchmark consists of two test cases:

1. **`lodash xor`**: This test case only includes the code `const res = _.xor(a, b)`. It appears that `_` refers to the Lodash library.
	* Lodash is a JavaScript utility library that provides various functions for tasks like string manipulation, array operations, and more.
	* In this context, `_.xor` likely performs an element-wise XOR operation between two arrays.

2. **`with xor function`**: This test case includes additional code: `function systemicDifference(arr1, arr2){...}`.
	* This function calculates the difference between two arrays by filtering out unique values that are present in both arrays.
	* The purpose of this comparison is to see how performance compares between Lodash's `xor` function and a custom implementation.

**Other Considerations**

When evaluating performance, we need to consider factors like:

* Cache locality: How well do the elements of `a` and `b` align in memory?
* Branch prediction: How often will the CPU branch between different execution paths?
* Loop unrolling: Is the loop optimized for better performance?

**Pros and Cons**

**Lodash's `_xor` function**

Pros:

* Likely to be highly optimized by Lodash developers.
* Will likely use SIMD instructions (Single Instruction, Multiple Data) for element-wise operations.

Cons:

* May not be as efficient due to overhead of the library itself.

**Custom `systemicDifference` implementation**

Pros:

* Can potentially optimize for specific hardware or micro-architectures.
* Eliminates library overhead.

Cons:

* Requires manual optimization and testing, which can be time-consuming.

**Benchmark Result Interpretation**

The benchmark result shows the performance of each test case across multiple executions. The results indicate that Lodash's `_xor` function is significantly faster than the custom implementation in this specific scenario.

Keep in mind that performance results can vary greatly depending on factors like hardware, software configurations, and system load.

**Alternatives**

If you were to rewrite the benchmark or create an alternative implementation, consider using:

* Other JavaScript libraries or frameworks that provide similar functions (e.g., `Array.prototype.reduce` instead of Lodash's `_xor`).
* A custom implementation optimized for performance on your specific hardware.
* Micro-benchmarking tools like Benchmark.js or micro-benchmarks to ensure accurate and reliable results.

By understanding the benchmark's setup, options, and considerations, you can create more effective comparisons between different implementations.

Related benchmarks:

lodash xor perf copy (version: 0)

Searching for faster solution for _.xor(a, b)

Comparing performance of: lodash xor vs with xor function

Created: one year ago by: Guest

Jump to the latest result

lodash xor

with xor function

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):