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ext math pow vs multiply
(version: 0)
Comparing performance of:
pow vs mult
Created:
5 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var DATA = []; for (var i = 0 ; i < 100; i++) { DATA.push(Math.random() * i); }
Tests:
pow
for (var i in DATA) { var x = Math.pow(DATA[i], 2); }
mult
for (var i in DATA) { var x = DATA[i] * DATA[i]; }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
pow
mult
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
gemma2:9b
, generated one year ago):
This benchmark compares two ways to calculate the square of a number: using `Math.pow()` and multiplying the number by itself. Here's a breakdown: **Options Compared:** * **`Math.pow(DATA[i], 2)`:** This uses the built-in `Math.pow()` function, which calculates the result of raising one number to the power of another. In this case, it squares the value from the `DATA` array. * **`DATA[i] * DATA[i]`:** This directly multiplies the value from the `DATA` array by itself. **Pros and Cons:** * **`Math.pow()`**: * **Pro:** Potentially more efficient for a wider range of exponents (not just squares). The `Math.pow()` function is likely optimized internally. * **Con:** Might be slightly slower for squaring due to the overhead of calling a separate function. * **Direct Multiplication**: * **Pro:** Simpler and more direct code. Less function call overhead. * **Con:** Not as versatile for calculating powers other than squares. **Other Considerations:** The benchmark measures *execution speed*. The results indicate that, in this specific case, multiplying directly (`DATA[i] * DATA[i]`) is slightly faster on the tested device/browser combination. **Alternatives:** * **Libraries:** Libraries like Lodash or NumJS could offer optimized math functions for performance-critical applications. However, they add an external dependency and might introduce slight overhead. * **Assembly/SIMD Instructions:** For truly maximum performance, you could explore using lower-level programming techniques like assembly language or SIMD (Single Instruction, Multiple Data) instructions to directly manipulate data at a faster rate. This is generally only necessary for highly demanding applications. Remember that benchmark results can vary significantly depending on the hardware, software, and specific code implementation.
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