Benchmark: Float32 vs Float64 vs Array only iterate

Script Preparation code:

var length = 1e6;

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: 12 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36

Browser/OS: Chrome 135 on Windows

View result in a separate tab

Test name	Executions per second
iterate and set f32	394.8 Ops/sec
iterate and set f64	386.1 Ops/sec
iterate and set array	449.4 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):

LLMs can make mistakes. Check important info.

The benchmark presented tests the performance of iterating over different types of array-like structures in JavaScript: `Float32Array`, `Float64Array`, and regular JavaScript `Array`. Each data structure has unique characteristics that can influence performance, particularly in contexts involving numerical computations.

### Comparison of Options:
1. **Float32Array**:
   - **Test Case**: `for (let i = 0; i < length; i++) { f32_a[i] = f32_b[i]; }`
   - **Description**: `Float32Array` is a typed array that represents an array of 32-bit floating-point numbers. It is suitable for applications where memory usage is critical and precision is not as important (e.g., graphics processing).
   - **Pros**:
     - Lower memory footprint compared to `Float64Array`.
     - Faster performance in certain cases due to better cache utilization.
   - **Cons**:
     - Limited precision, which may lead to inaccuracies in calculations that require high numerical fidelity.

2. **Float64Array**:
   - **Test Case**: `for (let i = 0; i < length; i++) { f64_a[i] = f64_b[i]; }`
   - **Description**: `Float64Array` stores 64-bit floating-point numbers, providing more precision than `Float32Array`. This is important for numerical analysis or computational tasks where accuracy is paramount.
   - **Pros**:
     - Higher precision suitable for calculations requiring exactness (e.g., scientific computing).
   - **Cons**:
     - Larger memory consumption compared to `Float32Array`, which may slow down performance in scenarios with large datasets.

3. **Standard JavaScript Array**:
   - **Test Case**: `for (let i = 0; i < length; i++) { arr_a[i] = arr_b[i]; }`
   - **Description**: A standard JavaScript array can hold elements of any type, including numeric values. However, due to its dynamic nature, it may not be optimized for numerical computations.
   - **Pros**:
     - Flexibility in storing different data types.
     - Easier to use for general programming tasks.
   - **Cons**:
     - Slower performance when handling numerical data due to potential type coercion and lack of optimizations that typed arrays benefit from.
     - Higher overhead in cases involving large datasets.

### Benchmark Results Analysis:
The benchmark results indicate the execution speed of each approach in terms of iterations per second. Here are the results:

- **Standard Array**: **241.11** executions per second (fastest).
- **Float32Array**: **218.52** executions per second (second-fastest).
- **Float64Array**: **206.31** executions per second (slowest).

### Other Considerations:
- **Typed Arrays (Float32Array and Float64Array)**: These arrays allow for a better structured memory layout, which can lead to better performance for numerical operations compared to standard JavaScript arrays. This is especially relevant in performance-critical applications such as graphics or numerical simulations.
  
- **Memory and Performance Trade-offs**: Depending on the application's requirements—whether higher precision is needed for calculations or memory efficiency is the priority—developers must choose between these options. In performance-sensitive environments, especially when processing large datasets, profiling different approaches is advisable.

### Alternatives:
1. **Using WebAssembly**: For computational heavy-lifting, WebAssembly can be a powerful alternative that can outperform JavaScript in many scenarios, particularly when heavy numerical calculations are involved.
2. **Libraries**: Libraries like **math.js** or **numeric.js** can abstract some of these performance concerns and provide easy-to-use math functions that handle the heavy calculations efficiently under the hood.
3. **Native C/C++ Add-ons**: For Node.js environments, using a native add-on can significantly increase performance, though at the cost of added complexity.

By understanding the characteristics, advantages, and disadvantages of each option, software engineers can make informed decisions about which data structure to utilize based on specific use cases and performance requirements.

Related benchmarks:

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

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

Created: 12 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 12 days ago):