Benchmark: UTF-8 vs UTF-16 WASM String Interface

HTML Preparation code:

<!--your preparation HTML code goes here-->

Script Preparation code:

const str_en = "Comparing the performance of UTF-8 vs UTF-16 string encoding copying into and out of WASM. 🙂";
const str_ar = "استعار جحا مرة آنية من جاره وعندما أعادها له أعاد معها آنية صغيرة";

const str_en_large = str_en.repeat(10);
const str_ar_large = str_ar.repeat(10);

const encoder = new TextEncoder('utf-8');
const decoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });

Tests:

str_en
const buf = encoder.encode(str_en); const str = decoder.decode(buf);
str_ar
const buf = encoder.encode(str_ar); const str = decoder.decode(buf);
str_en_large
const buf = encoder.encode(str_en_large); const str = decoder.decode(buf);
str_ar_large
const buf = encoder.encode(str_ar_large); const str = decoder.decode(buf);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
str_en
str_ar
str_en_large
str_ar_large

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; rv:136.0) Gecko/20100101 Firefox/136.0

Browser/OS: Firefox 136 on Windows

View result in a separate tab

Test name	Executions per second
str_en	6557358.5 Ops/sec
str_ar	2509458.5 Ops/sec
str_en_large	1376575.0 Ops/sec
str_ar_large	516037.7 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON is focused on the performance comparison of UTF-8 and UTF-16 string encoding in the context of WebAssembly (WASM) when executing in a JavaScript environment. The essential aspect being measured is the efficiency of encoding and decoding strings in different languages (English and Arabic) using these two encoding formats.

### Options Compared

1. **String Encoding**:
   - **UTF-8**: A variable-length character encoding that can use one to four bytes for each character. It's backward compatible with ASCII and is the standard encoding for the web.
   - **UTF-16**: A variable-length character encoding that typically uses two bytes for most common characters (like those in the English alphabet) but can also use four bytes for certain characters.

2. **Test Cases**:
   - The benchmark consists of four test cases:
     - **str_en**: Encoding and decoding a short English string.
     - **str_ar**: Encoding and decoding a short Arabic string.
     - **str_en_large**: Encoding and decoding a long English string (the short string repeated ten times).
     - **str_ar_large**: Encoding and decoding a long Arabic string (also repeated ten times).

### Performance Metrics

For each of these test cases, the benchmark measures the number of executions per second, which indicates how quickly the encoding and decoding processes occur for the respective strings.

### Results Overview

The collected results from the latest benchmark show the following executions per second for each test case when run on Firefox:

- **str_en**: 6,557,358.5
- **str_ar**: 2,509,458.5
- **str_en_large**: 1,376,575.0
- **str_ar_large**: 516,037.71875

### Pros and Cons of Encoding Approaches

**UTF-8 Pros**:
- Compact for English text and many other scripts.
- Widely used and compatible with ASCII, making it ideal for web applications.
- Reduces bandwidth requirements for primarily English content.

**UTF-8 Cons**:
- More complex processing for certain characters since it uses a variable number of bytes.
- Performance may vary with string size and complexity.

**UTF-16 Pros**:
- Can be more efficient for languages that predominantly use characters that fall within the Basic Multilingual Plane, such as many Asian scripts.
- Easier to handle for processing because its fixed-width representation (for most characters) simplifies character indexing.

**UTF-16 Cons**:
- Increased memory usage for text that is primarily in English since it uses two bytes per character unnecessarily.
- Compatibility issues with some web standards that prefer UTF-8.

### Considerations for Using WASM

The WebAssembly (WASM) environment allows for highly optimized execution of code. In this benchmark, the use of the UTF-8 and UTF-16 encoding coupled with WASM highlights performance characteristics that are important for applications that require frequent string manipulations, such as text editors, language processing tools, or applications relying on multiple languages.

### Alternative Approaches

1. **Direct Binary Representation**: For certain applications, especially those that involve numeric data or well-defined structures, using direct binary representations rather than encoded strings can improve performance.

2. **Other Encoding Libraries**: There are several libraries available for string encoding/decoding (e.g., `iconv-lite` for Node.js) which may have different performance attributes. These libraries can provide additional functionalities like handling various encodings more efficiently.

3. **Choosing Encoding Based on Context**: Developers might choose to standardize on one encoding (like UTF-8) unless there are specific requirements that necessitate UTF-16, as this helps avoid potential encoding errors and optimizes performance for most web scenarios.

4. **WebAssembly Optimized Libraries**: Some libraries are specifically designed to leverage WASM's performance characteristics, which may provide better encoding/decoding performance compared to standard JavaScript implementations.

In conclusion, the benchmark highlights the different performance traits of UTF-8 and UTF-16 encoding when processed through a WASM interface, allowing developers to make informed decisions based on performance considerations tailored to their specific application requirements.

Related benchmarks:

UTF-8 vs UTF-16 WASM String Interface (version: 1)

Comparing performance of: str_en vs str_ar vs str_en_large vs str_ar_large

Created: one month ago by: Registered User

Jump to the latest result

str_en

str_ar

str_en_large

str_ar_large

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):