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)
}

​x
 
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))
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)))

 
 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)))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
current xor implementation
alternative with Sets

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 16 days 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

Test name	Executions per second
current xor implementation	1053.6 Ops/sec
alternative with Sets	6459.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

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:

Current Implementation: This is the original implementation using Lodash's xor function.
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:

Using SIMD Instructions: Implementing XOR using Single Instruction, Multiple Data (SIMD) instructions could potentially provide significant speedups on modern CPUs.
Native Array Operations: Leveraging native array operations like map(), filter(), and reduce() might be faster than using a library function.
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.

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.

Related benchmarks:

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: 4 years ago by: Registered User

Jump to the latest result

current xor implementation

alternative with Sets

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):