Benchmark: math.abs vs branchless absolute val

Script Preparation code:

eval("");
var abs = Math.abs, x = -2131.1283712;

Tests:

branchless
eval(""); return (x < 0) * -x + (x >= 0) * x;
native
eval(""); return abs(x);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
branchless
native

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its components.

**Benchmark Definition**

The benchmark definition is provided in JSON format, which includes:

*   `Name`: A unique identifier for the benchmark ("math.abs vs branchless absolute val").
*   `Description`: An optional description of the benchmark (null in this case).
*   `Script Preparation Code`: The JavaScript code that sets up the environment before running the benchmark. In this case, it uses `eval()` to create a scope and assigns the `abs` variable to the result of calling `Math.abs(x)`. It also initializes the `x` variable with a negative value.
*   `Html Preparation Code`: Another optional section for HTML preparation code, which is empty in this case.

**Individual Test Cases**

The benchmark has two test cases:

1.  **Branchless**: The first test case has a benchmark definition that uses a branchless implementation of absolute value calculation: `(x < 0) * -x + (x >= 0) * x`. This approach avoids using the `Math.abs()` function and instead uses bitwise operations to achieve the same result.
2.  **Native**: The second test case uses the native `Math.abs()` function.

**Options Compared**

The two test cases compare:

*   Branchless vs Native: These are the two approaches being tested for absolute value calculation in JavaScript.

**Pros and Cons of Each Approach**

Branchless approach:

Pros:

*   Potentially faster execution speed due to fewer function calls
*   May be more cache-friendly

Cons:

*   More complex bitwise operations can be harder to read and maintain
*   Might not work correctly if the input value is NaN (Not a Number) or Infinity

Native `Math.abs()` approach:

Pros:

*   Widely supported and well-documented, making it easier to understand and implement
*   Works correctly with all numerical types in JavaScript
*   Typically more efficient than branchless implementations due to compiler optimizations

Cons:

*   May incur a small overhead due to the function call and potential caching issues
*   Might not be as fast as optimized branchless implementations for specific use cases

**Library Used**

There is no explicit library used in this benchmark. However, it's worth noting that `Math.abs()` is a built-in JavaScript method.

**Special JS Features or Syntax**

This benchmark does not explicitly use any special JavaScript features or syntax beyond what's required to run the test cases. The branchless implementation uses bitwise operations, which are a standard part of the language.

**Alternatives**

Other alternatives for implementing absolute value calculation in JavaScript include:

*   Using the `Math.abs()` function with a fallback for older browsers that may not support it
*   Implementing an absolute value function from scratch using bitwise operations or other methods
*   Utilizing third-party libraries or polyfills to provide a more robust and efficient implementation

Keep in mind that these alternatives might not be as well-suited for every use case, especially if performance is a top priority.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:108.0) Gecko/20100101 Firefox/108.0

Browser/OS: Firefox 108 on Windows

View result in a separate tab

Test name	Executions per second
branchless	1929170.0 Ops/sec
native	1591366.8 Ops/sec

Related benchmarks:

math.abs vs branchless absolute val (version: 0)

Comparing performance of: branchless vs native

Created: 2 years ago by: Guest

Jump to the latest result

branchless

native

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):