Benchmark: DataView vs TypedArray Float64 2

Script Preparation code:

var buffer1 = new ArrayBuffer(1024 * Float64Array.BYTES_PER_ELEMENT);
var array = new Float64Array(buffer1);
var buffer2 = new ArrayBuffer(1024 * Float64Array.BYTES_PER_ELEMENT);
var view = new DataView(buffer2);

Tests:

array[i] = i;
for (let i = 0; i < 1024; i += 1) { if (i === 0) { array[i] = 1; } else { array[i] = array[i-1] + i;; } }
view.setFloat64(i, i)
for (let i = 0; i < 1024; i += 1) { if (i === 0) { view.setFloat64(i, 1); } else { view.setFloat64(i, view.getFloat64(i - 1) + i); } }
array[i] = i; 2
for (let i = 0; i < 1024; i += 1) { if (i === 0) { array[i] = 1; } else { array[i] = array[i-1] + i;; } }
view.setFloat64(i, i) 2
for (let i = 0; i < 1024; i += 1) { if (i === 0) { view.setFloat64(i, 1); } else { view.setFloat64(i, view.getFloat64(i - 1) + i); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array[i] = i;
view.setFloat64(i, i)
array[i] = i; 2
view.setFloat64(i, i) 2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36

Browser/OS: Chrome 108 on Windows

View result in a separate tab

Test name	Executions per second
array[i] = i;	193591.4 Ops/sec
view.setFloat64(i, i)	46397.5 Ops/sec
array[i] = i; 2	222832.0 Ops/sec
view.setFloat64(i, i) 2	51341.1 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):

LLMs can make mistakes. Check important info.

The provided benchmark evaluates the performance of two different approaches to managing and manipulating binary data in JavaScript: **Typed Arrays** (specifically `Float64Array`) and the **DataView** object. Their performance is assessed based on executing a series of similar operations involving storing and retrieving 64-bit floating-point numbers. The tests analyze the speed of executing operations over 1024 iterations using both approaches.

### 1. **Benchmark Overview**
- **Typed Arrays** (`Float64Array`):
  - Typed Arrays are array-like objects in JavaScript that provide a mechanism for reading and writing to binary data buffers more efficiently.
  - The `Float64Array` specifically allows for working with 64-bit floating-point numbers, which is significant for tasks that require high precision, such as high-performance calculations in data processing and graphics.

- **DataView**:
  - A DataView provides a way to read and write different numeric types (like Float64, Uint32, etc.) from an `ArrayBuffer` regardless of the endianness of the underlying platform.
  - It is more flexible than Typed Arrays as it allows you to manipulate different data types from the same buffer, but this flexibility comes at a potential cost in performance.

### 2. **Test Cases**
The benchmark contains four individual test cases, two for `Float64Array` and two for `DataView`, which compare their performance through similar operations:

- **Test Case: `array[i] = i;`** and its duplicate:
  - This operation assigns values into the `Float64Array`, with the first index set to 1 and subsequent indices calculated based on the previous index plus the current iteration (`i`).
  
- **Test Case: `view.setFloat64(i, i)`** and its duplicate:
  - This operation uses the `DataView` to set values in the same manner as the Typed Array, but it retrieves the previous value via `view.getFloat64(i - 1)` before performing the addition.

### 3. **Performance Results**
The results from the benchmark detail the number of executions per second for each of the test cases:
- The results show that operations using `Float64Array` (`array[i] = i;`) outperformed the `DataView` operations (`view.setFloat64(i, i)`) significantly in this context:
  - `array[i] = i; 2`: 222,832 executions/second
  - `array[i] = i;`: 193,591 executions/second
  - `view.setFloat64(i, i) 2`: 51,341 executions/second
  - `view.setFloat64(i, i)`: 46,397 executions/second

### 4. **Pros and Cons**

- **Typed Arrays**:
  - **Pros**:
    - Generally faster for sequential operations over known data types (like Float64).
    - More straightforward API when working with a fixed data type.
  - **Cons**:
    - Limited to a single data type per array instance; requires a new array for different types.

- **DataView**:
  - **Pros**:
    - Greater flexibility by allowing mixed data types within the same buffer and the ability to handle endianness explicitly.
    - Suitable for low-level manipulations where data type varies.
  - **Cons**:
    - Slower performance in this specific benchmark due to additional overhead in method calls (e.g., `getFloat64`, `setFloat64`).

### 5. **Alternatives**
While the benchmark focuses on Typed Arrays and DataView, there are other alternatives depending on the use case:
- **ArrayBuffer** with simple arrays: if strict typing or performance isn't critical.
- **WebAssembly**: For applications demanding highly optimized performance for numerical processing, WebAssembly can be a better alternative, allowing users to execute low-level code with near-native performance.
- **Third-party Libraries**: For more complex data structures and operations, frameworks like NumPy (via Pyodide in a Python context) or libraries inclusive of TypedArray enhancements might provide additional functionality beyond the native capabilities.

This benchmark ultimately reflects the trade-offs between performance and flexibility in JavaScript when managing binary data. Understanding when to use each approach can enable developers to write faster and more efficient code in the right scenarios.

Related benchmarks:

DataView vs TypedArray Float64 2 (version: 1)

Comparing performance of: array[i] = i; vs view.setFloat64(i, i) vs array[i] = i; 2 vs view.setFloat64(i, i) 2

Created: one month ago by: Guest

Jump to the latest result

array[i] = i;

view.setFloat64(i, i)

array[i] = i; 2

view.setFloat64(i, i) 2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):