Benchmark: DataView or BitTwiddling for Reads

Script Preparation code:

let buf = new ArrayBuffer(12);
u32s = new Uint32Array(buf);
u8s = new Uint8Array(buf);
dv = new DataView(buf);
offset = 0;
sum = 0;

Tests:

Bit Twiddling, Aligned
sum += ((u8s[offset] << 24) | (u8s[offset + 1] << 16) | (u8s[offset + 2] << 8) | u8s[offset + 3]) | 0;
DataView, Aligned
sum += dv.getInt32(offset, false);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Bit Twiddling, Aligned
DataView, Aligned

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases to understand what is being tested.

**Benchmark Definition**

The benchmark definition consists of two main parts: "DataView or BitTwiddling for Reads" and its script preparation code. The script preparation code sets up an ArrayBuffer, creates two Uint8Array views from it (u32s and u8s), and a DataView view (dv) with the same buffer.

**Options Compared**

The benchmark compares two approaches:

1. **DataView**: Using the `DataView` API to access the buffer.
2. **BitTwiddling**: Performing bit-level operations directly on the `Uint8Array` views.

**Pros and Cons**

**DataView Approach:**

Pros:

* More readable and maintainable code, as it uses a high-level API to manipulate the data.
* Less prone to errors, as the DataView handles data type conversions and alignment automatically.

Cons:

* May incur overhead due to the use of a higher-level API.
* Might not be optimized for performance by the browser engine.

**BitTwiddling Approach:**

Pros:

* Can potentially lead to better performance, as it bypasses the abstraction layer of the `DataView` API.
* Often used in low-level optimization and embedded systems development.

Cons:

* More difficult to read and maintain, due to the need to manually perform bit-level operations.
* Prone to errors, especially when dealing with alignment and data type conversions.

**Other Considerations**

* The benchmark assumes that the `DataView` API is optimized for performance in the browser engine. If this is not the case, the results may be influenced by the overhead of the DataView.
* The choice between DataView and BitTwiddling might depend on the specific use case and requirements.

**Library Usage**

The library used here is none explicit mentioned, but we can see that `Uint8Array` and `DataView` are part of the JavaScript standard library.

**Special JS Feature or Syntax**

There are no special JS features or syntax used in this benchmark. The code uses only standard JavaScript features, such as ArrayBuffer, Uint8Array, and DataView views.

**Other Alternatives**

If you wanted to test a different approach, here are some alternatives:

* **Native Array**: Instead of using `Uint8Array`, you could use the native `Array` type to represent an array of 32-bit integers.
* **Indexed Arrays**: You could use indexed arrays (e.g., `let x = new Int32Array(buf)` instead of `let u32s = new Uint32Array(buf)`) for a different performance profile.

However, keep in mind that using native types or indexed arrays might not provide the same benefits as the `Uint8Array` and `DataView` approach in terms of code readability and maintainability.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36

Browser/OS: Chrome 116 on Linux

View result in a separate tab

Test name	Executions per second
Bit Twiddling, Aligned	866768.5 Ops/sec
DataView, Aligned	2008789.0 Ops/sec

Related benchmarks:

DataView or BitTwiddling for Reads (version: 0)

Is DataView faster than directly bit twiddling?

Comparing performance of: Bit Twiddling, Aligned vs DataView, Aligned

Created: one year ago by: Registered User

Jump to the latest result

Bit Twiddling, Aligned

DataView, Aligned

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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