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Math.pow(x,0.5) vs Math.sqrt(x) vs (x ** 0.5) sum in for of loop
(version: 0)
Comparing performance of:
Math.sqrt vs sqrt with Math.pow vs sqrt with ** 0.5
Created:
2 years ago
by:
Guest
Go to the latest result
Script Preparation code:
var numbers = Array.from(Array(10000), (_,x) => (Math.random()*x));
Tests:
Math.sqrt
let sum = 0 for (number of numbers) sum += Math.sqrt(number);
sqrt with Math.pow
let sum = 0; for (number of numbers) sum += Math.pow(number,0.5);
sqrt with ** 0.5
let sum = 0; for (number of numbers) sum += number ** 0.5;
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
Math.sqrt
sqrt with Math.pow
sqrt with ** 0.5
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/605.1.15 (KHTML, like Gecko) Version/17.4.1 Safari/605.1.15
Browser/OS:
Safari 17 on Mac OS X 10.15.7
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=521783" width="100%" height="500px"></iframe>
sqrt with ** 0.5
769/s
sqrt with Math.pow
556/s
Math.sqrt
554/s
View exact numbers
Test name
Executions per second
✓
sqrt with ** 0.5
769 Ops/sec
sqrt with Math.pow
556 Ops/sec
Math.sqrt
554 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'll explain the provided benchmark and its options. **Benchmark Definition** The provided JSON represents a JavaScript microbenchmark that compares three different ways to calculate the square root of numbers in a loop: 1. `Math.sqrt(x)`: Using the built-in `sqrt` function from the Math library. 2. `(x ** 0.5)`: Using exponentiation with a fractional exponent (0.5). 3. `Math.pow(number, 0.5)`: Using the `pow` function from the Math library to calculate the square root. **Options Comparison** The three options are compared in terms of performance, specifically the number of executions per second on a Desktop platform running Safari 17. The benchmark measures how quickly each option can execute the loop without producing any errors or crashes. Here's a brief analysis of each option: 1. **`Math.sqrt(x)`**: * Pros: Built-in function, likely optimized for performance. * Cons: May not be as efficient due to the overhead of the `sqrt` function call. 2. **`(x ** 0.5)`**: * Pros: Simple and direct exponentiation approach, potentially faster than built-in functions. * Cons: Uses fractional exponents, which may require additional calculations, reducing performance. 3. **`Math.pow(number, 0.5)`**: * Pros: Another built-in function, likely optimized for performance, but uses `pow`, which might incur some overhead compared to direct exponentiation. * Cons: Similar to the first option, relies on a built-in function. In general, using built-in functions like `sqrt` or `pow` can be faster due to optimizations and cache benefits. However, in this case, the difference is relatively small, indicating that the choice of method has a limited impact on performance. **Library Use** The first option (`Math.sqrt(x)`) uses the Math library's `sqrt` function, which is a built-in JavaScript function for calculating square roots. The second and third options use exponentiation with fractional exponents `(x ** 0.5)` and `Math.pow(number, 0.5)`, respectively. **Special JS Feature or Syntax** There are no special JavaScript features or syntax mentioned in this benchmark. All the code uses standard JavaScript syntax and built-in functions. **Other Alternatives** For a more comprehensive comparison of different square root calculation methods, you could consider adding options like: * Using a library specifically designed for mathematical operations (e.g., `lodash`). * Implementing the algorithm manually using bitwise operations or other techniques. * Comparing performance with different data types (e.g., integers vs. floats). Keep in mind that these alternatives might introduce additional complexity and potentially deviate from the original benchmark's focus on simple, JavaScript-specific optimizations. I hope this explanation helps you understand the provided benchmark!
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