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simplify formula vs non-simplify
(version: 0)
Comparing performance of:
non-simplify vs simplify
Created:
3 years ago
by:
Guest
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Tests:
non-simplify
var x = 32.8 - 4*32.8*Math.pow((0.15 - 0.5), 2);
simplify
var y = -4 * 32.8 * (0.15*0.15 - 0.15)
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Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
non-simplify
simplify
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark JSON and explain what's being tested. **Benchmark Definition** The benchmark definition is simply a mathematical expression that defines two different formulas: 1. `simplify`: `var y = -4 * 32.8 * (0.15*0.15 - 0.15)` 2. `non-simplify`: `var x = 32.8 - 4*32.8*Math.pow((0.15 - 0.5), 2)` These formulas are used to test the performance difference between two approaches: simplifying and not simplifying the expressions. **Options being compared** In this benchmark, we have two options: 1. **Simplify**: Using the simplified formula, which is expected to be faster since it involves fewer operations. 2. **Non-simplify**: Using the original, unsimplified formula, which is expected to be slower due to the additional operations involved. **Pros and Cons of each approach** * **Simplifying expressions**: + Pros: Typically faster execution times, as there are fewer operations to perform. + Cons: May require more complex calculations or use of mathematical libraries, which can introduce overhead. * **Not simplifying expressions**: + Pros: Can be easier to implement and understand, as the original formula is not modified. + Cons: Expected to be slower than simplified formulas due to the additional operations. **Library and special features** The provided benchmark code doesn't explicitly mention any libraries or special JavaScript features. However, it does use some built-in functions like `Math.pow` and arithmetic operators (`*`, `-`, etc.). **Other alternatives** If you were to create a similar benchmark, you might consider adding more options, such as: 1. Using different mathematical libraries (e.g., `Math.js`) or frameworks. 2. Implementing the expressions using different programming paradigms (e.g., imperative vs. functional). 3. Comparing performance with different compiler optimizations enabled. Keep in mind that benchmarking is an iterative process, and results may vary depending on the specific use case, hardware, and software configurations.
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