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Math.pow vs Exponentiation vs Shift
(version: 0)
Comparing performance of:
Math.pow vs Exponentiation vs Shift
Created:
2 years ago
by:
Guest
Go to the latest result
Script Preparation code:
var n = 12;
Tests:
Math.pow
var x = Math.pow(2, n);
Exponentiation
var y = 2 ** n;
Shift
var y = 2 << n
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
Math.pow
Exponentiation
Shift
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 years ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36
Browser/OS:
Chrome 119 on Mac OS X 10.15.7
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=512644" width="100%" height="500px"></iframe>
Shift
21.3M/s
Exponentiation
20.7M/s
Math.pow
7.3M/s
View exact numbers
Test name
Executions per second
✓
Shift
21,343,140 Ops/sec
Exponentiation
20,745,090 Ops/sec
Math.pow
7,296,675 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested. **Benchmark Overview** The benchmark is designed to compare the performance of three different methods for calculating exponentiation: `Math.pow()`, exponentiation (`2 ** n`), and left shift (`2 << n`). The goal is to determine which method is the fastest in a specific scenario. **Options Compared** The three options being compared are: 1. `Math.pow(n, 2)` (exponentiation using the built-in `Math.pow()` function) 2. `2 ** n` (exponentiation using the exponentiation operator) 3. `2 << n` (shift operation) **Pros and Cons of Each Approach** Here's a brief summary of the pros and cons of each approach: 1. **`Math.pow(n, 2)`**: This is the most straightforward way to calculate exponentiation. However, it may not be the fastest due to the overhead of calling a built-in function. * Pros: Easy to read and understand * Cons: May have performance overhead 2. **`2 ** n`**: This approach uses the exponentiation operator, which is available in modern JavaScript engines. It's often faster than `Math.pow()` because it doesn't incur the same overhead. * Pros: Fast and efficient * Cons: May not be supported by older browsers or versions of JavaScript 3. **`2 << n`**: This approach uses a shift operation to calculate exponentiation. While it may seem unusual, it can be faster than `Math.pow()` for certain inputs due to the way bitwise operations are optimized. * Pros: Fast and efficient * Cons: May not be immediately clear to readers who aren't familiar with this technique **Library or Special JS Feature** None of these options rely on a specific library, but they do use some advanced JavaScript features: 1. **`Math.pow()`**: This function is part of the standard JavaScript API and doesn't require any additional libraries. 2. **Exponentiation Operator (`**`)**: This operator is available in modern JavaScript engines (ECMAScript 2020+). If you're targeting older browsers or versions, you may need to use a polyfill or fall back to `Math.pow()`. 3. **Left Shift (`<<`)**: This operator is also part of the standard JavaScript API and doesn't require any additional libraries. **Other Considerations** When choosing an approach for this benchmark, consider the following factors: * Performance: If speed is critical, the exponentiation operator or left shift might be a better choice. * Readability: If code readability is more important than performance, `Math.pow()` might be a better option. * Compatibility: If you need to support older browsers or versions of JavaScript, you may need to use `Math.pow()` instead. **Alternatives** If you're looking for alternative ways to calculate exponentiation, consider the following options: 1. **Binary Exponentiation**: This is an optimized algorithm that uses bit manipulation to calculate exponentiation more efficiently than using shift operations. 2. **Fast Exponentiation Algorithms**: There are various algorithms available, such as the "Exponentiation by squaring" algorithm, which can be used for more complex calculations involving exponents. These alternatives might provide better performance or improved readability, but they may also introduce additional complexity or dependencies on specific libraries or frameworks.
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