Benchmark: Math.pow(2,n) vs Table lookup vs bitwise

Script Preparation code:

var POWERS = [
		1,2,4,8,16,32,64,128,256,512,1024,2048,4096,8192,16384,32768,65536,131072,262144,524288,1048576,2097152,4194304,8388608,16777216,33554432
];

Tests:

Table lookup
let cnt = POWERS.length; let x; for(let i =0;i<cnt;i++){ x = POWERS[i]; }
Math.pow
let cnt = POWERS.length; let x; for(let i =0;i<cnt;i++){ x = Math.pow(2,i); }
bitwise (1 << n)
let cnt = POWERS.length; let x; for(let i =0;i<cnt;i++){ x = (1 << i); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Table lookup
Math.pow
bitwise (1 << n)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

Measuring performance differences in JavaScript can be quite interesting. Let's dive into the provided benchmark definition and explain what is tested, compare different approaches, and consider pros and cons.

**Benchmark Definition**

The provided JSON defines a simple benchmarking scenario where three test cases are compared:

1. **Table lookup**: This test uses an array (`POWERS`) to store power-of-2 values, which are then iterated over using a `for` loop to retrieve the corresponding value.
2. **Math.pow**: This test uses the built-in `Math.pow` function to calculate powers of 2 directly.
3. **Bitwise operation (1 << n)**: This test uses bitwise shift operators (`<<`) to calculate powers of 2.

**Options Comparison**

The three test cases differ in how they access and retrieve power-of-2 values:

* **Table lookup**: Iterates over an array of pre-defined power-of-2 values using a `for` loop.
* **Math.pow**: Directly calculates the desired power-of-2 value using the built-in `Math.pow` function.
* **Bitwise operation (1 << n)**: Uses bitwise shift operators to calculate powers of 2 directly.

**Pros and Cons**

Here's a brief summary of the pros and cons for each approach:

* **Table lookup**: Pros:
	+ Pre-computes power-of-2 values in advance, reducing runtime overhead.
	+ Allows for easy modification or extension of the power set.
* Cons:
	+ Requires storing and managing an array of large numbers (power-of-2 values).
	+ May incur additional memory allocation and deallocation costs.
* **Math.pow**: Pros:
	+ Eliminates the need for pre-computation and storage of power-of-2 values.
	+ Provides a simple, straightforward way to calculate powers of 2.
* Cons:
	+ Can be slower due to the overhead of function calls and potential rounding errors.
	+ May not be suitable for very large or dynamic ranges of power-of-2 values.
* **Bitwise operation (1 << n)**: Pros:
	+ Generally faster than `Math.pow` due to optimized bitwise operations.
	+ Scalable for large ranges of power-of-2 values without significant performance degradation.
* Cons:
	+ Requires understanding of bitwise shift operators and their behavior.
	+ May not be as readable or maintainable as other approaches.

**Library Usage**

The provided benchmark definition uses the `POWERS` array, which is a static variable defined in the `Script Preparation Code`. This array contains pre-computed power-of-2 values up to `1024 * 1024`.

**Special JavaScript Features/Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark definition. However, bitwise shift operators (`<<`) and `Math.pow` are essential concepts that may require additional explanation for readers without deep knowledge of JavaScript.

**Other Alternatives**

Alternative approaches to test the performance differences could include:

* Using a dynamic range of power-of-2 values instead of an array.
* Implementing a lookup table using a different data structure, such as a hash table or binary search tree.
* Comparing the performance of different optimized libraries or implementations for `Math.pow` and bitwise shift operators.

In summary, this benchmark definition tests three common approaches to calculating powers of 2 in JavaScript: pre-computing and storing an array of values (table lookup), using the built-in `Math.pow` function, and employing bitwise shift operators. The results can help developers understand the performance implications of each approach and make informed decisions about which one to use depending on their specific requirements.

Latest run results:

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

User agent: Mozilla/5.0 (Linux; Android 13) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Mobile Safari/537.36

Browser/OS: Chrome Mobile 113 on Android

View result in a separate tab

Test name	Executions per second
Table lookup	273325.5 Ops/sec
Math.pow	145618.7 Ops/sec
bitwise (1 << n)	6254165.0 Ops/sec

Related benchmarks:

Math.pow(2,n) vs Table lookup vs bitwise (version: 0)

Which is faster: Table lookup or Math.pow(2,value) or bitwise operation (1 << value)?

Comparing performance of: Table lookup vs Math.pow vs bitwise (1 << n)

Created: one year ago by: Guest

Jump to the latest result

Table lookup

Math.pow

bitwise (1 << n)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):