Benchmark: Test uint32array ant biguint64array

Script Preparation code:

var size = 8 * 40;
var srcBuf8 = new ArrayBuffer(size);
var srcBufPtr8 = new Uint8Array(srcBuf8);
var srcBuf16 = new ArrayBuffer(size*2);
var srcBufPtr16 = new Uint16Array(srcBuf16);
var srcBuf32 = new ArrayBuffer(size*4);
var srcBufPtr32 = new Uint32Array(srcBuf32);
var srcBuf64 = new ArrayBuffer(size*8);
var srcBufPtr64 = new BigUint64Array(srcBuf64);

for (let i = 0; i < size; i++) {
    srcBufPtr8[i] = 100 * Math.random();
}
for (let i = 0; i < size; i++) {
    srcBufPtr16[i] = srcBufPtr8[i];
}
for (let i = 0; i < size; i++) {
    srcBufPtr32[i] = srcBufPtr8[i];
}
for (let i = 0; i < size; i++) {
    srcBufPtr64[i] = BigInt(srcBufPtr8[i]);
}

Tests:

Uint32Array
let smallerCount = 0; for (let i = 0; i < (size-1); i++) { if (srcBufPtr32[i] < srcBufPtr32[i+1]) { smallerCount++; } }
Biguint64array
let smallerCount = 0; for (let i = 0; i < (size-1); i++) { if (srcBufPtr64[i] < srcBufPtr64[i+1]) { smallerCount++; } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Uint32Array
Biguint64array

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 3 days ago):

LLMs can make mistakes. Check important info.

The benchmark you've provided tests the performance of two different typed arrays in JavaScript: **`Uint32Array`** and **`BigUint64Array`**. The purpose of this benchmark is to compare the speed of sequential access and comparison operations between these two data structures, specifically how many times it can execute a counting operation where it checks if the previous element is smaller than the next one.

### Options Compared

1. **`Uint32Array`**:
   - This typed array represents an array of 32-bit unsigned integers. 
   - Here, the benchmark code counts how many times a larger value follows a smaller one in the array of `Uint32Array`.

2. **`BigUint64Array`**:
   - This typed array represents an array of 64-bit unsigned integers, allowing for representation of much larger integers than `Uint32Array`.
   - Similarly, the benchmark counts occurrences of a smaller number followed by a larger one in the array of `BigUint64Array`.

### Pros and Cons

**`Uint32Array`:**
- **Pros**:
  - Easier to use and understand for operations that only require integers within the range of 32 bits (0 to 4,294,967,295).
  - Potentially faster performance on operations and less memory overhead compared to `BigUint64Array` in scenarios where the data fits within its limits.
- **Cons**:
  - Cannot represent numbers larger than 4,294,967,295. If needed, it might lead to overflow errors.

**`BigUint64Array`:**
- **Pros**:
  - Can represent a much broader range of integers (0 to 18,446,744,073,709,551,615), which is useful for applications needing to handle big integers, such as certain cryptographic functions or large data processing.
- **Cons**:
  - May involve more memory overhead due to larger size (8 bytes per entry) compared to `Uint32Array` (4 bytes per entry).
  - Potentially slower for operations that do not require the high capacity since it needs more time and resources to manage larger integers.

### Other Considerations
- **Memory Footprint**: Since `Uint32Array` uses less memory than `BigUint64Array`, it may be preferred in scenarios where a large array is needed, and data fits the 32-bit unsigned integer requirement.
- **Use Cases**: Choosing between these arrays will depend on the specific use case. For most applications where integer values don't exceed 4 billion, `Uint32Array` is sufficient. In contrast, if integer math or representation of very large values is needed, `BigUint64Array` is beneficial despite potential performance trade-offs.
  
### Alternatives
Apart from `Uint32Array` and `BigUint64Array`, there are several other typed arrays available in JavaScript:
- **`Int32Array`**: This is similar to `Uint32Array` but allows signed integers (positive and negative).
- **`Float32Array` / `Float64Array`**: Useful for applications requiring floating-point computations.
- **Other Typed Arrays**: Such as `Int8Array`, `Uint8Array`, `Uint16Array`, and specialized arrays for different data types depending on the need.

### Conclusion
This benchmark effectively illustrates the performance differences between two important data structures in JavaScript, allowing developers to make informed choices based on their specific requirements regarding performance, memory usage, and value range. Understanding the strengths and limitations of these typed arrays can be essential for optimizing performance in JavaScript applications.

Latest run results:

Run details: (Test run date: 3 days ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 16_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.2 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 16 on iOS 16.2

View result in a separate tab

Test name	Executions per second
Uint32Array	14701.2 Ops/sec
Biguint64array	12380.2 Ops/sec

Related benchmarks:

Test uint32array ant biguint64array (version: 1)

Test

Comparing performance of: Uint32Array vs Biguint64array

Created: 3 days ago by: Guest

Jump to the latest result

Uint32Array

Biguint64array

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 3 days ago):