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Keep a number between boundaries: Math Min+Max vs Ifs (fixed)
(version: 1)
Comparing performance of:
Min and Max vs Ifs
Created:
one year ago
by:
Guest
Go to the latest result
HTML Preparation code:
<!--your preparation HTML code goes here-->
Script Preparation code:
this.value = Math.random();
Tests:
Min and Max
Math.min(Math.max(0.3, this.value), 0.7);
Ifs
if(this.value < 0.3) return 0.3 if(this.value > 0.7) return 0.7 return this.value
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Min and Max
Ifs
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Mobile Safari/537.36
Browser/OS:
Chrome Mobile 131 on Android
View result in a separate tab
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=577826" width="100%" height="500px"></iframe>
Min and Max
21.1M/s
Ifs
9.4M/s
View exact numbers
Test name
Executions per second
✓
Min and Max
21,051,130 Ops/sec
Ifs
9,436,401 Ops/sec
Autogenerated LLM Summary
(model
gpt-4o-mini
, generated one year ago):
The benchmark titled "Keep a number between boundaries: Math Min+Max vs Ifs (fixed)" compares two approaches for clamping a random number within specified boundaries (in this case, between 0.3 and 0.7). ### Approaches Compared 1. **Using Math.min and Math.max**: - **Test Name**: Min and Max - **Benchmark Definition**: `Math.min(Math.max(0.3, this.value), 0.7);` - **Description**: This approach uses the `Math.min` and `Math.max` functions. First, it ensures that the value is not less than 0.3 using `Math.max`, and then it ensures that the result is not greater than 0.7 using `Math.min`. - **Pros**: - Concise and readable syntax. - Potentially optimized by JavaScript engines for performance since it is a built-in function. - **Cons**: - Slightly more overhead since it requires two function calls, although typically this is negligible in most scenarios. 2. **Using If Statements**: - **Test Name**: Ifs - **Benchmark Definition**: ```javascript if(this.value < 0.3) return 0.3; if(this.value > 0.7) return 0.7; return this.value; ``` - **Description**: This approach involves conditional checks using `if` statements. It checks if the value is less than 0.3 and returns 0.3, checks if it is greater than 0.7 and returns 0.7, otherwise, it returns the value as is. - **Pros**: - Straightforward logic that some may find easier to understand. - May perform well in scenarios where values are consistently within bounds since it could short-circuit. - **Cons**: - More verbose and potentially less readable. - Can lead to slower execution as it involves branching, which might hinder the CPU's ability to optimize pipeline execution. ### Performance Results The results show that the "Min and Max" method executed approximately 21,051,130 times per second, while the "Ifs" method executed around 9,436,401 times per second. This indicates that, at least in this benchmark scenario using the Chrome Mobile 131 browser on an Android platform, the `Math.min` and `Math.max` approach is significantly faster than using `if` statements. ### Other Considerations and Alternatives - **Use Cases**: The choice of method can depend on performance requirements and personal or team coding standards. For example, if performance is critical and the range is tightly controlled, `Math.min` and `Math.max` might be preferred. - **Alternative Libraries**: While the benchmark itself doesn't rely on any external libraries, developers may consider using utility libraries such as Lodash or Underscore.js, which provide various utility functions, including functions to clamp numbers. These libraries can offer improved readability and potentially optimized implementations but could introduce additional overhead. - **Native Language Features**: Some newer language features or syntax could also help, but in this benchmark, the approaches are traditional JavaScript without any novel ES6+ features being employed. In conclusion, the benchmark highlights a classic performance optimization scenario. While both approaches are valid, choosing the right method may depend on specific use cases, readability preferences, and performance requirements in the context of the application being developed.
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