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cool stuff with x*x and stuff
(version: 1)
Comparing performance of:
x*x vs sqrt with Math.pow vs 2*x
Created:
one year ago
by:
Guest
Go to the latest result
Script Preparation code:
var numbers = Array.from(Array(10000), (_,x) => (Math.random()*x));
Tests:
x*x
numbers.forEach(x => x*x);
sqrt with Math.pow
numbers.forEach(x => Math.pow(x,2));
2*x
numbers.forEach(x => 2*x);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
Fork
Test case name
Result
x*x
sqrt with Math.pow
2*x
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/131.0.0.0 Safari/537.36 Edg/131.0.0.0
Browser/OS:
Chrome 131 on Windows
View result in a separate tab
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=570674" width="100%" height="500px"></iframe>
x*x
386K/s
2*x
385K/s
sqrt with Math.pow
21K/s
View exact numbers
Test name
Executions per second
✓
x*x
385,525 Ops/sec
2*x
384,719 Ops/sec
sqrt with Math.pow
21,175 Ops/sec
Autogenerated LLM Summary
(model
gpt-4o-mini
, generated one year ago):
The benchmark defined in the provided JSON focuses on evaluating the performance of different mathematical operations performed on an array of randomly generated numbers. Here’s a breakdown of the benchmark, the options being compared, and the pros and cons of each approach. ### Benchmark Overview The benchmark script prepares an array called `numbers` containing 10,000 random values, generated using the `Math.random()` method. The benchmark tests the performance of three different mathematical operations applied to each number in the array. ### Test Cases 1. **Test Case: x*x** - **Expression:** `numbers.forEach(x => x*x);` - This test case uses direct multiplication of each number (`x`) by itself. - **Pros:** This approach is straightforward and typically fast due to optimized multiplication operators in JavaScript engines. - **Cons:** While efficient, it may not be as semantically clear to some readers, though this is generally negligible in performance. 2. **Test Case: sqrt with Math.pow** - **Expression:** `numbers.forEach(x => Math.pow(x,2));` - This test case calculates the square of each number by using the `Math.pow` function. - **Pros:** This method is highly readable and demonstrates the use of the `Math` library for exponentiation. - **Cons:** The use of `Math.pow` is generally slower than direct multiplication due to the overhead of the function call and the internal handling of the operation, making this option the least performant in the test. 3. **Test Case: 2*x** - **Expression:** `numbers.forEach(x => 2*x);` - This test case demonstrates scalar multiplication, multiplying each number by 2. - **Pros:** Like direct multiplication, this approach is simple and highly optimized in JavaScript engines. It demonstrates efficiency when scaling values without the overhead of more complex operations. - **Cons:** The semantic meaning is different from squaring a number, which might be important depending on the context. ### Performance Results Comparison The benchmark results clearly show different performance metrics for each test case in terms of executions per second: - **x*x**: 385524.59 executions per second - **2*x**: 384718.72 executions per second - **sqrt with Math.pow**: 21174.90 executions per second From these results, it is evident that both direct multiplication (`x*x`) and the scalar multiplication by 2 (`2*x`) are highly efficient, while the use of `Math.pow` results in significantly lower performance, demonstrating the overhead associated with function calls in JavaScript. ### Other Considerations and Alternatives While this benchmark focuses on JavaScript, similar performance evaluations can be conducted in other languages such as Python or C++. For example, in Python, one might use list comprehensions or the built-in `map` function for similar theoretical comparisons. **Alternatives include:** - Using libraries such as `lodash` for more complex operations which might not be as performant as native JavaScript methods. - Implementing WebAssembly for compute-intensive tasks might also be relevant depending on the application scope. - Exploring newer JavaScript features, like the use of `Math.hypot()` or special math libraries, for different mathematical operations. In summary, this benchmark provides clear insights into mathematical operations' performance in JavaScript, thereby helping software engineers make informed decisions regarding which method to use based on both readability and execution efficiency.
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