Benchmark: ArrayBuffer - MeasureThat.net

Script Preparation code:

var str = "aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7";

var chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";

// Use a lookup table to find the index.
var lookup = new Uint8Array(256);
for (var i = 0; i < chars.length; i++) {
  lookup[i] = chars.charCodeAt(i);
}

function _base64ToArrayBufferFaster(base64) {
    var binary_string =  window.atob(base64);
    var len = binary_string.length;
    var bytes = new Uint8Array( len );
    for (var i = 0; i < len; i++)        {
        bytes[i] = lookup[binary_string[i]];
    }
    return bytes.buffer;
}

function _base64ToArrayBuffer(base64) {
    var binary_string =  window.atob(base64);
    var len = binary_string.length;
    var bytes = new Uint8Array( len );
    for (var i = 0; i < len; i++)        {
        bytes[i] = binary_string.charCodeAt(i);
    }
    return bytes.buffer;
}

Tests:

Cached decode
_base64ToArrayBufferFaster(str);
Simple decode
_base64ToArrayBuffer(str);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Cached decode
Simple decode

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down what is tested on the provided JSON that represents the benchmark.

**Benchmark Definition**
The benchmark is testing two different approaches to decoding base64 strings in JavaScript:

1. `_base64ToArrayBufferFaster(base64)`
2. `_base64ToArrayBuffer(base64)`

These functions are designed to convert a base64 string into an `ArrayBuffer` object, which can be used for various tasks such as image processing or cryptographic operations.

**Options Compared**

The two options being compared are:

* Using a lookup table (`lookup`) to find the index of each character in the `chars` string.
* Not using a lookup table and instead using the `charCodeAt()` method to get the ASCII value of each character in the base64 string.

**Pros and Cons**

**Using a Lookup Table ( `_base64ToArrayBufferFaster` )**

Pros:

* Faster execution time, as it reduces the number of function calls and uses a cached lookup table.
* More efficient use of memory, as it doesn't require creating multiple arrays for each iteration.

Cons:

* Requires more code to implement and maintain, as it involves creating and populating the lookup table.
* May have a higher overhead due to the additional memory allocation and caching mechanism.

**Not Using a Lookup Table ( `_base64ToArrayBuffer` )**

Pros:

* Simpler implementation, as it only requires a single function call for each character in the base64 string.
* Less overhead due to fewer memory allocations and no caching required.

Cons:

* Slower execution time, as it involves multiple `charCodeAt()` calls and potential string indexing issues.
* May lead to more memory allocation and deallocation, resulting in higher memory usage.

**Other Considerations**

* The use of `window.atob()` function for base64 decoding is not explicitly mentioned in the benchmark definition. However, it's likely that this function uses a similar lookup table or implementation as `_base64ToArrayBufferFaster`.
* The benchmark does not account for potential issues such as null or undefined input strings, which should be handled using additional error checking and handling code.

**Alternative Approaches**

Other possible approaches to decoding base64 strings in JavaScript include:

1. Using a library like `buffer` or `base64-js`, which provide optimized implementations for base64 decoding.
2. Implementing a custom base64 decoder using bitwise operations and arithmetic, without relying on lookup tables or caching.
3. Using Web Workers or parallel processing to distribute the base64 decoding workload across multiple cores or threads.

It's worth noting that these alternative approaches may have different trade-offs in terms of performance, memory usage, and code complexity, which should be carefully evaluated depending on the specific requirements and constraints of the project.

Related benchmarks:

ArrayBuffer (version: 0)

Comparing performance of: Cached decode vs Simple decode

Created: 9 years ago by: Guest

Jump to the latest result

Cached decode

Simple decode

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):