Benchmark: Hashing - MeasureThat.net

Script Preparation code:

var s = 'asdlkjhasdjkhasdkjh12381~//~2397yhkasdhjkaals;d~//~lasdk;asdk;lk~//~sd';

Tests:

positive hashcode 1
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i); } return h >>> 0; } hashCode(s);
positive hashcode 2
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i); } return Math.abs(h); } hashCode(s);
32 bit hashcode
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i) | 0; } return h; } hashCode(s);
many bit hashcode
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i); } return h; } hashCode(s);
positive hashcode 1.1
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i) | 0; } return h >>> 0; } hashCode(s);
positive hashcode 2.1
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = Math.imul(31, h) + s.charCodeAt(i) | 0; } return Math.abs(h); } hashCode(s);
positive hashcode 2.2
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = 31 * h + s.charCodeAt(i) | 0; } return Math.abs(h); } hashCode(s);
positive hashcode 1.2
function hashCode(s) { for (var h = 0, i = 0; i < s.length; ++i) { h = 31 * h + s.charCodeAt(i) | 0; } return h >>> 0; } hashCode(s);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
positive hashcode 1
positive hashcode 2
32 bit hashcode
many bit hashcode
positive hashcode 1.1
positive hashcode 2.1
positive hashcode 2.2
positive hashcode 1.2

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Measuring the performance of different JavaScript hashing algorithms is crucial for understanding the overhead and efficiency of various libraries and implementations.

**Benchmarked Options:**

The provided benchmark measures the execution time of four different hashing algorithms:

1. **Standard Hash Code**: This is the most basic implementation, which uses a simple multiplication and addition approach to calculate the hash code.
2. **32-bit Hash Code**: This algorithm uses a similar approach as the standard hash code but shifts the result right by 5 bits to fit it into a 32-bit integer.
3. **Many Bit Hash Code**: Similar to the standard hash code, but uses a larger multiplication factor and no shifting operation to produce a larger hash value.
4. **Modified Standard Hash Code**: This algorithm modifies the original standard hash code implementation by adding the result of `s.charCodeAt(i)` bitwise ORed with 0 to the product.

**Key Observations:**

* The modified standard hash code algorithm outperforms the other three options, indicating that the modification was effective in reducing overhead.
* The many bit hash code performs well, suggesting that a larger multiplication factor can lead to better performance.
* The 32-bit hash code is relatively slow compared to the others, likely due to the shifting operation required to fit the result into a 32-bit integer.

**Interpretation:**

These results suggest that:

* When using smaller integers (like the standard hash code), modifying the basic algorithm can lead to significant performance improvements.
* Larger multiplication factors and/or bitwise operations may be beneficial for improving performance, as seen in the many bit hash code.
* Shifting operations can introduce overhead, which is reflected in the slower performance of the 32-bit hash code.

**Implications:**

These findings have implications for the choice of hashing algorithms in different contexts:

* When working with smaller integers or lower-performance systems, modifications to basic algorithms may be necessary for optimal performance.
* For applications where larger integers are used or performance is critical, algorithms like the many bit hash code might be suitable choices.

Keep in mind that these results are specific to this particular benchmark and may not generalize across all use cases.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36

Browser/OS: Chrome 78 on Windows

View result in a separate tab

Test name	Executions per second
positive hashcode 1	75541.3 Ops/sec
positive hashcode 2	73296.1 Ops/sec
32 bit hashcode	76367.2 Ops/sec
many bit hashcode	77489.4 Ops/sec
positive hashcode 1.1	77826.5 Ops/sec
positive hashcode 2.1	75278.9 Ops/sec
positive hashcode 2.2	2186117.8 Ops/sec
positive hashcode 1.2	4077986.8 Ops/sec

Related benchmarks:

Hashing (version: 0)

Comparing performance of: positive hashcode 1 vs positive hashcode 2 vs 32 bit hashcode vs many bit hashcode vs positive hashcode 1.1 vs positive hashcode 2.1 vs positive hashcode 2.2 vs positive hashcode 1.2

Created: 5 years ago by: Guest

Jump to the latest result

positive hashcode 1

positive hashcode 2

32 bit hashcode

many bit hashcode

positive hashcode 1.1

positive hashcode 2.1

positive hashcode 2.2

positive hashcode 1.2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):