Benchmark: Float32 vs Float64 vs Array only iterate1e4

Script Preparation code:

var length = 1e4;

var f32_a = Float32Array.from({length}, Math.random);
var f32_b = Float32Array.from({length}, Math.random);

var f64_a = Float64Array.from({length}, Math.random);
var f64_b = Float64Array.from({length}, Math.random);

var arr_a = Array.from({length}, Math.random);
var arr_b = Array.from({length}, Math.random);

Tests:

iterate and set f32
for (let i = 0; i < length; i++) { f32_a[i] = f32_b[i]; }
iterate and set f64
for (let i = 0; i < length; i++) { f64_a[i] = f64_b[i]; }
iterate and set array
for (let i = 0; i < length; i++) { arr_a[i] = arr_b[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
iterate and set f32
iterate and set f64
iterate and set array

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 14 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36

Browser/OS: Chrome 135 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
iterate and set f32	27402.2 Ops/sec
iterate and set f64	30106.9 Ops/sec
iterate and set array	35373.9 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 14 days ago):

LLMs can make mistakes. Check important info.

The provided JSON benchmark tests the performance of three different approaches for iterating over arrays and assigning values in JavaScript. Specifically, it compares the performance of using Float32Arrays, Float64Arrays, and standard JavaScript arrays (Array) in a simple iteration-and-set operation.

### Options Compared

1. **Float32Array**:
   - **Benchmark Definition**: 
     ```javascript
     for (let i = 0; i < length; i++) {
       f32_a[i] = f32_b[i];
     }
     ```
   - **Pros**:
     - Smaller memory footprint (4 bytes per element vs. 8 bytes for Float64) can be advantageous, especially in performance-critical applications involving large data sets.
     - Faster when performing operations on large datasets where precision can be sacrificed.
   - **Cons**:
     - Limited precision (7 decimal digits), which may lead to rounding errors in certain computations and may not be suitable for applications requiring high precision in floating-point operations.

2. **Float64Array**:
   - **Benchmark Definition**: 
     ```javascript
     for (let i = 0; i < length; i++) {
       f64_a[i] = f64_b[i];
     }
     ```
   - **Pros**:
     - Higher precision (15-17 decimal digits), making it suitable for applications requiring accurate floating-point calculations (e.g., financial applications).
   - **Cons**:
     - Larger memory footprint (8 bytes per element), which may impact performance if data sets are large, and can lead to increased memory usage.

3. **Array**:
   - **Benchmark Definition**: 
     ```javascript
     for (let i = 0; i < length; i++) {
       arr_a[i] = arr_b[i];
     }
     ```
   - **Pros**:
     - More flexible and easy to use, supporting more diverse types of data.
     - JavaScript arrays offer dynamic sizing and can store mixed data types, which can lead to more versatile programming.
   - **Cons**:
     - Generally slower in performance for numerical operations compared to typed arrays like Float32Arrays and Float64Arrays due to type coercion and overhead in handling the dynamic size and multiple data types.

### Benchmark Results

Based on the most recent benchmark results:

- The **standard Array** (arr_a[i] = arr_b[i]) achieved the highest execution rate at approximately **35,374 executions per second**.
- The **Float64Array** option followed with about **30,107 executions per second**.
- The **Float32Array** was the lowest performer at roughly **27,402 executions per second**.

This indicates that, for the specific operation tested (iterating and setting values), traditional JavaScript arrays may outperform typed arrays. However, this could change based on the operation's complexity and the size of the data being processed, where performance characteristics might differ.

### Other Considerations

When deciding between these options:

- **Application Requirements**: The choice will largely depend on specific application needs—precision versus performance and memory usage.
  
- **Browser Compatibility**: Typed arrays (Float32Array and Float64Array) are well-supported across modern browsers, but compatibility should still be verified if supporting older browsers.

- **Optimization**: If the application frequently manipulates large datasets, it may benefit from using typed arrays for performance-critical sections.

### Alternatives

Other alternatives for data manipulation in JavaScript include:

1. **Typed Arrays**: Beyond Float32 and Float64, JavaScript also has Int8Array, Int16Array, Uint8Array, etc., which can be useful depending on the data type requirements.

2. **WebAssembly (WASM)**: For more computationally intensive tasks, WebAssembly can dramatically improve performance by allowing code to run close to native speeds.

3. **Libraries**: Libraries such as [NumPy](https://numpy.org/) (in Python) or [math.js](https://mathjs.org/) can provide enhanced performance for mathematical operations and may provide the features necessary for complex operations that standard JS arrays or typed arrays do not.

In conclusion, the choice of data structure and performance technique depends on the application's specific requirements for speed, precision, and memory usage, and each option serves different scenarios effectively.

Related benchmarks:

Float32 vs Float64 vs Array only iterate1e4 (version: 1)

Comparing performance of: iterate and set f32 vs iterate and set f64 vs iterate and set array

Created: 14 days ago by: Guest

Jump to the latest result

iterate and set f32

iterate and set f64

iterate and set array

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 14 days ago):