Benchmark: Hashing algo - MeasureThat.net

Script Preparation code:

var str = '[["backgroundColor","rgba(0, 0, 0, 0.003)"],["border","none"],["boxShadow","inset 0 -2px 1px rgba(0, 0, 0, 0.03)"],["boxSizing","border-box"],["color","inherit"],["fontFamily","inherit"],["fontSize","24px"],["fontWeight","inherit"],["height","65px"],["lineHeight","1.4em"],["margin","0"],["outline","none"],["padding","16px 16px 16px 60px"],["placeholder",{"fontStyle":"italic","fontWeight":300,"color":"#e6e6e6"}],["position","relative"],["width","100%"]]';
function MurmurHash1(key, seed = 0) {
    var m = 3332679571, h = Math.imul(key.length, m) ^ seed;

    for(var i = 0, b = key.length & -4; i < b; i += 4) {
        h += key[i+3] << 24 | key[i+2] << 16 | key[i+1] << 8 | key[i];
        h = Math.imul(h, m); h ^= h >>> 16;
    }
    
    switch(key.length & 3) {
        case 3: h += key[i+2] << 16;
        case 2: h += key[i+1] << 8;
        case 1: h += key[i];
                h = Math.imul(h, m); h ^= h >>> 16;
    }

    h = Math.imul(h, m); h ^= h >>> 10;
    h = Math.imul(h, m); h ^= h >>> 17;

    return h >>> 0;
};
function Cyrb53(key, seed = 0) {
  const A = 2654435761;
  const B = 1597334677;
  const C = 2246822507;
  const D = 3266489909;
  const E = 4294967296;
  const F = 2097151;

  let h1 = 0xdeadbeef ^ seed;
  let h2 = 0x41c6ce57 ^ seed;

  for (let index = 0, char; index < key.length; index++) {
    char = key.charCodeAt(index);

    h1 = Math.imul(h1 ^ char, A);
    h2 = Math.imul(h2 ^ char, B);
  }

  h1 = Math.imul(h1 ^ (h1 >>> 16), C) ^ Math.imul(h2 ^ (h2 >>> 13), D);
  h2 = Math.imul(h2 ^ (h2 >>> 16), C) ^ Math.imul(h1 ^ (h1 >>> 13), D);

  return E * (F & h2) + (h1 >>> 0);
};
function SuperFastHash(key) {
    if (!key) {return 0}
    var hash = key.length, tmp, len = key.length >>> 2, p = 0;

    for (var i = 0; i < len; i++) {
        hash += key[p] | key[p+1] << 8;
        tmp = ((key[p+2] | key[p+3] << 8) << 11) ^ hash;
        hash = (hash << 16) ^ tmp;
        hash += hash >>> 11;
        p += 4;
    }

    switch(key.length & 3) {
        case 3:
            hash += key[p] | key[p+1] << 8;
            hash ^= hash << 16;
            hash ^= key[p+2] << 18;
            hash += hash >>> 11;
            break;
        case 2:
            hash += key[p] | key[p+1] << 8;
            hash ^= hash << 11;
            hash += hash >>> 17;
            break;
        case 1:
            hash += key[p];
            hash ^= hash << 10;
            hash += hash >>> 1;
            break;
    }

    hash ^= hash << 3;
    hash += hash >>> 5;
    hash ^= hash << 4;
    hash += hash >>> 17;
    hash ^= hash << 25;
    hash += hash >>> 6;

    return hash >>> 0;
};

Tests:

MurmurHash1
MurmurHash1(str);
Cyrb53
Cyrb53(str);
SuperFastHash
SuperFastHash(str);

Historical Performance Data aggregated from 38 community runs

Desktop Results:

Chrome 28 runs

SuperFastHash

137K/s

MurmurHash1

78K/s

Cyrb53

20K/s

Fastest: SuperFastHash (6.8x faster)

View exact numbers

✓ SuperFastHash: 136,753 ops/sec
• MurmurHash1: 78,060 ops/sec
• Cyrb53: 20,101 ops/sec

Firefox 10 runs

Cyrb53

617K/s

SuperFastHash

302K/s

MurmurHash1

226K/s

Fastest: Cyrb53 (2.7x faster)

View exact numbers

✓ Cyrb53: 616,519 ops/sec
• SuperFastHash: 302,222 ops/sec
• MurmurHash1: 225,921 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
MurmurHash1
Cyrb53
SuperFastHash

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/149.0.0.0 Safari/537.36 Edg/149.0.0.0

Browser/OS: Chrome 149 on Windows

View result in a separate tab

Cyrb53

5.4M/s

MurmurHash1

96K/s

SuperFastHash

94K/s

View exact numbers

Test name	Executions per second
✓ Cyrb53	5,438,741 Ops/sec
MurmurHash1	95,723 Ops/sec
SuperFastHash	93,551 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition JSON**

The benchmark definition defines three JavaScript functions: `MurmurHash1`, `Cyrb53`, and `SuperFastHash`. These functions are used to hash strings. The benchmark is run with a predefined input string `str` passed as an argument to each function.

**Individual Test Cases**

Each test case represents one of the three functions being compared:

1. **MurmurHash1**: This implementation by Scott Long uses a combination of bitwise operations and modular arithmetic to generate a hash value.
2. **Cyrb53**: This implementation by Eric A. Schmidt uses a different approach with bitwise operations, rotations, and shifts to generate a hash value.
3. **SuperFastHash**: This is another implementation ( likely written by John Resig) that takes advantage of the `length` property of strings in JavaScript.

**Comparison Options**

The benchmark compares the performance of each function:

* **MurmurHash1**
* **Cyrb53**
* **SuperFastHash**

These functions have different design goals and optimization strategies, which affect their performance:

* **MurmurHash1**: Designed for speed, with an emphasis on minimizing operations. It's a simple, yet efficient implementation.
* **Cyrb53**: Optimized for security, using more complex calculations to generate a hash value. While it's slower than MurmurHash1, it provides better resistance to collisions.
* **SuperFastHash**: Optimized for speed and memory usage, as it reuses the `length` property of strings in JavaScript.

**Pros and Cons**

Here are some pros and cons of each approach:

* **MurmurHash1**
	+ Pros: Fast, simple implementation
	+ Cons: May not be suitable for security-critical applications due to its simplicity
* **Cyrb53**
	+ Pros: Provides better resistance to collisions, suitable for security-critical applications
	+ Cons: Slower than MurmurHash1 due to more complex calculations
* **SuperFastHash**
	+ Pros: Optimized for speed and memory usage
	+ Cons: May not provide the same level of security as Cyrb53

**Other Considerations**

When choosing a hash function, consider factors such as:

* Speed: How fast do you need the hash to be?
* Security: Do you require resistance to collisions or other attacks?
* Memory usage: Is memory an issue for your application?

In summary, MurmurHash1 is optimized for speed, Cyrb53 provides better security but at a cost in terms of performance, and SuperFastHash balances speed and memory usage. The choice ultimately depends on the specific requirements of your application.

Hashing algo (version: 0) Benchmark.js

Comparing performance of: `MurmurHash1 vs Cyrb53 vs SuperFastHash`

Created: 3 years ago by: Guest

Go to the latest result

MurmurHash1

Cyrb53

SuperFastHash

Historical Performance Data aggregated from 38 community runs

Desktop Results:

Chrome 28 runs

Firefox 10 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Hashing algo (version: 0) Benchmark.js

Comparing performance of: MurmurHash1 vs Cyrb53 vs SuperFastHash

Created: 3 years ago by: Guest

Go to the latest result

MurmurHash1

Cyrb53

SuperFastHash

Historical Performance Data aggregated from 38 community runs

Desktop Results:

Chrome 28 runs

Firefox 10 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: `MurmurHash1 vs Cyrb53 vs SuperFastHash`