Benchmark: lodash xor alternatives

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:

current xor implementation
const res = _.xor(a, _.xor(a,b))
alternative with Sets
var as = new Set(a) var bs = new Set(b) const res = a .filter((row_id) => bs.has(row_id)) .concat(b.filter((row_id) => !as.has(row_id)))

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

alternative with Sets

9K/s

current xor implementation

2K/s

Fastest: alternative with Sets (4.6x faster)

View exact numbers

✓ alternative with Sets: 9,325 ops/sec
• current xor implementation: 2,016 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
current xor implementation
alternative with Sets

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:137.0) Gecko/20100101 Firefox/137.0

Browser/OS: Firefox 137 on Linux

View result in a separate tab

alternative with Sets

6K/s

current xor implementation

1K/s

View exact numbers

Test name	Executions per second
✓ alternative with Sets	6,459 Ops/sec
current xor implementation	1,054 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is searching for faster solution for `_.xor(a, _.xor(a,b))`, which is a common operation in JavaScript that computes the bitwise XOR (exclusive or) of two arrays. The goal is to find a more efficient implementation than the current one used by Lodash.

**Options Compared**

Two options are compared:

1. **Current Implementation**: This is the original implementation using Lodash's `xor` function.
2. **Alternative with Sets**: This option uses sets (a data structure that stores unique values) to efficiently compute the XOR operation.

**Pros and Cons of Each Approach**

**Current Implementation**

Pros:

* Familiarity: It uses a well-known library, making it easier for developers to understand and implement.
* Low Overhead: The implementation is likely optimized for performance.

Cons:

* Slow: Lodash's `xor` function may be slower than other implementations due to its complexity.

**Alternative with Sets**

Pros:

* Efficient: Using sets allows for an efficient computation of the XOR operation, reducing time complexity.
* Scalable: This approach can handle large arrays without significant performance degradation.

Cons:

* Steeper Learning Curve: Developers need to understand how to use sets and implement this logic.
* Higher Overhead: Creating sets may have additional overhead due to memory allocation and iteration.

**Library Used (Set)**

The `Set` data structure is used in the alternative implementation. Sets are a native JavaScript data structure that stores unique values, allowing for efficient membership testing and iteration. In this case, creating two sets (`as` and `bs`) allows for a fast lookup of elements in both arrays.

**Special JS Feature/Syntax**

There doesn't appear to be any special JavaScript features or syntax used in the benchmark.

**Other Alternatives**

Other alternatives to improve performance might include:

1. **Using SIMD Instructions**: Implementing XOR using Single Instruction, Multiple Data (SIMD) instructions could potentially provide significant speedups on modern CPUs.
2. **Native Array Operations**: Leveraging native array operations like `map()`, `filter()`, and `reduce()` might be faster than using a library function.
3. **Hand-Optimized Implementation**: Writing an optimized implementation in C++ or Rust, then wrapping it in JavaScript, could potentially provide significant speedups.

These alternatives would require more extensive investigation and testing to determine their feasibility and effectiveness.

lodash xor alternatives (version: 0)

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

Comparing performance of: `current xor implementation vs alternative with Sets`

Created: 5 years ago by: Registered User

Go to the latest result

current xor implementation

alternative with Sets

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

lodash xor alternatives (version: 0)

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

Comparing performance of: current xor implementation vs alternative with Sets

Created: 5 years ago by: Registered User

Go to the latest result

current xor implementation

alternative with Sets

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `current xor implementation vs alternative with Sets`