Benchmark: test ABS 3 - MeasureThat.net

Script Preparation code:

var callMathAbs = Math.abs
var some_float = -0.00034254146579137945;
var cycle_counter = 10000000;

Tests:

Math
for(i=0; i<cycle_counter; i++){ var result = Math.abs(some_float); }
Custom
for(i=0; i<cycle_counter; i++){ var negative = some_float < 0.0 ? 1 : 0; var result = some_float - some_float * 2.0 * negative; }
Custom2
for(i=0; i<cycle_counter; i++){ var result = some_float & -1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math
Custom
Custom2

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Windows

View result in a separate tab

Test name	Executions per second
Math	0.3 Ops/sec
Custom	0.3 Ops/sec
Custom2	0.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition**

The benchmark is defined by two scripts: `Script Preparation Code` and `Html Preparation Code`. The `Script Preparation Code` sets up variables, including `callMathAbs`, `some_float`, and `cycle_counter`. `cycle_counter` is set to 10,000,000. The `Html Preparation Code` is empty in this case.

The benchmark appears to measure the performance of different approaches for calculating the absolute value of a number, specifically:

1. Using the built-in `Math.abs()` function (Benchmark Definition: "for(i=0; i<cycle_counter; i++){\r\n  var result = Math.abs(some_float);\r\n}")
2. A custom approach using bitwise operations to determine the sign of the input number and then perform the absolute value calculation (Benchmark Definition: "for(i=0; i<cycle_counter; i++){\r\n  var negative = some_float < 0.0 ? 1 : 0;\r\n  var result = some_float - some_float * 2.0 * negative;\r\n}")
3. Another custom approach using bitwise operations to simply mask the bits of the input number (Benchmark Definition: "for(i=0; i<cycle_counter; i++){\r\n  var result = some_float & -1;\r\n}")

**Options Compared**

The three options compared are:

* `Math.abs()`: uses the built-in absolute value function from JavaScript's Math library.
* Custom Approach 1 (using conditional statements and multiplication): performs a custom calculation to determine the sign of the input number and then takes its absolute value.
* Custom Approach 2 (using bitwise operations): uses bitwise operations to mask the bits of the input number, effectively performing the same operation as `Math.abs()` but using different techniques.

**Pros and Cons**

Here are some pros and cons for each approach:

1. `Math.abs()`:
	* Pros: simple, widely supported, and well-tested.
	* Cons: may be slower than custom approaches due to function call overhead.
2. Custom Approach 1:
	* Pros: can potentially be faster than `Math.abs()` since it avoids the function call overhead.
	* Cons: more complex, and error-prone if not implemented correctly.
3. Custom Approach 2 (bitwise operations):
	* Pros: potentially fast since bitwise operations are often optimized by the CPU.
	* Cons: less intuitive and harder to read than `Math.abs()` or Custom Approach 1.

**Libraries**

There is no library explicitly mentioned in the benchmark definition. However, it's worth noting that some custom approaches might rely on external libraries for additional functionality (e.g., the conditional statement approach might use a library like jQuery).

**Special JS Features/Syntax**

None are explicitly mentioned in this benchmark. If they were used, I would explain them and their implications on performance.

**Alternatives**

Some alternative approaches to measuring absolute value could include:

* Using `Math.abs()` with a loop or array comprehension (e.g., `Array.from(Array(cycle_counter).keys()).map(() => Math.abs(some_float))`)
* Implementing the custom bitwise operations approach using a library like V8's inline assembly APIs (if available)
* Using a third-party library like `fast-absolute` which provides an optimized implementation of absolute value calculations

Keep in mind that these alternatives might not be relevant to this specific benchmark, and their performance characteristics may vary depending on the use case.

Related benchmarks:

test ABS 3 (version: 0)

Comparing performance of: Math vs Custom vs Custom2

Created: one year ago by: Guest

Jump to the latest result

Math

Custom

Custom2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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