Benchmark: dfdxstraight 2 - MeasureThat.net

Script Preparation code:

const { cos, pow, PI, sin, sqrt } = Math
const sq = x => x*x

function dfdxStraightNew (method, cBlow, Dcable, lCable, cableCount, Wn, Bn, Dduct, cof, A, P, pressure, pressureHead, slope, x, y) {
  const psonic = (1 - cBlow) * pressure
  let WB = 0
  let TEF = 0
  if (A / 100 > (Dduct - Dcable) / 2000) {
    WB = 3 * (A / 100 - Dduct / 2000 + Dcable / 2000) * Bn / 2 / pow(P / 4, 4)
    TEF = 8 * PI * (A / 100 - Dduct / 2000 + Dcable / 2000) / (P * P)
  }
  const Buckle = (Dduct - Dcable) / (1000 * PI * PI * Bn)

  let pp = 0
  if (method === 'jetting') {
    pp = PI * Dcable * Dduct / 8 / lCable * (pow(pressure + 1, 2) - pow(psonic + pressureHead + 1, 2)) / sqrt(pow(pressure + 1, 2) - (pow(pressure + 1, 2) - pow(psonic + pressureHead + 1, 2)) * x / lCable) / 10
  } else if (method === 'waterpushpull' || method === 'floating' || method === 'freefloating') {
    pp = PI * Dcable * Dduct / 4 / lCable * (pressure - psonic - pressureHead) / 10 * cableCount * (Dduct + Dcable) / (Dduct + cableCount * Dcable)
  }

  if (y > 0) {
    return cof * sqrt(pow(Wn * cos(slope), 2) + pow(TEF * y + WB, 2)) + Wn * sin(slope) - pp
  } else {
    return cof * sqrt(pow(Wn * cos(slope), 2) + pow(TEF * y + WB, 2) + pow(Buckle * y * y, 2)) + Wn * sin(slope) - pp
  }
}

function dfdxStraightNew2 (method, cBlow, Dcable, lCable, cableCount, Wn, Bn, Dduct, cof, A, P, pressure, pressureHead, slope, x, y) {
  const psonic = (1 - cBlow) * pressure
  let WB = 0
  let TEF = 0
  if (A / 100 > (Dduct - Dcable) / 2000) {
    WB = 3 * (A / 100 - Dduct / 2000 + Dcable / 2000) * Bn / 2 / pow(P / 4, 4)
    TEF = 8 * PI * (A / 100 - Dduct / 2000 + Dcable / 2000) / (P * P)
  }
  const Buckle = (Dduct - Dcable) / 1000 / (PI * PI) / Bn

  let pp = 0
  if (method === 'jetting') {
    pp = PI * Dcable * Dduct / 8 / lCable * (sq(pressure + 1) - sq(psonic + pressureHead + 1)) / sqrt(sq(pressure + 1) - (sq(pressure + 1) - sq(psonic + pressureHead + 1)) * x / lCable) / 10
  } else if (method === 'waterpushpull' || method === 'floating' || method === 'freefloating') {
    pp = PI * Dcable * Dduct / 4 / lCable * (pressure - psonic - pressureHead) / 10 * cableCount * (Dduct + Dcable) / (Dduct + cableCount * Dcable)
  }

  if (y > 0) {
    return cof * sqrt(sq(Wn * cos(slope)) + sq(TEF * y + WB)) + Wn * sin(slope) - pp
  } else {
    return cof * sqrt(sq(Wn * cos(slope)) + sq(TEF * y + WB) + sq(Buckle * y * y)) + Wn * sin(slope) - pp
  }
}

Tests:

pow
dfdxStraightNew('jetting', 1, 19, 2437, 1, 2.941994999999995, 9.5, 42, 0.1092, 5, 10, 12, 0.33886251591322436, 0, 1001.3587388228319, 9.17014015195002)
sq
dfdxStraightNew2('jetting', 1, 19, 2437, 1, 2.941994999999995, 9.5, 42, 0.1092, 5, 10, 12, 0.33886251591322436, 0, 1001.3587388228319, 9.17014015195002)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pow
sq

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**Benchmark Definition**

The provided JSON represents a benchmark definition, which is a script that defines a mathematical function `dfdxStraightNew` (and its variant `dfdxStraightNew2`) that takes several input parameters and returns a result. The function appears to be related to a specific problem in fluid dynamics, likely involving pipe flow or acoustic analysis.

**Options Compared**

The two functions `dfdxStraightNew` and `dfdxStraightNew2` differ primarily in the way they calculate the `pp` value, which is used in the final calculation of the result. Specifically:

* In `dfdxStraightNew`, `pp` is calculated using a more complex formula involving trigonometric functions (`cos`, `sin`, `tan`) and algebraic manipulations.
* In `dfdxStraightNew2`, `pp` is calculated using a simpler formula involving squaring (`sq`) instead of trigonometric functions.

**Pros and Cons**

The choice between these two approaches depends on various factors, such as:

* **Performance**: The `dfdxStraightNew` function might be faster due to the reduced number of trigonometric operations. However, this comes at the cost of increased complexity and potentially slower compilation times.
* **Readability and Maintainability**: The `dfdxStraightNew2` approach is likely easier to understand and maintain, as it relies on well-established mathematical formulas rather than custom algebraic manipulations.
* **Specialized Libraries or Features**: It's unclear whether any specialized libraries (e.g., MathJS) or features in the browser engine would be used to optimize these calculations. If such optimizations are available, they might favor one approach over the other.

**Individual Test Cases**

The two test cases provided measure the performance of the `pow` and `sq` functions, respectively, using different instances of the `dfdxStraightNew` function.

* The first test case measures the performance of the more complex `dfdxStraightNew` function with the `jetting` method.
* The second test case measures the performance of the simpler `dfdxStraightNew2` function with the `jetting` method.

**Latest Benchmark Result**

The latest benchmark result shows that:

* Firefox 114 achieves approximately 14.3 million executions per second for the `pow` function using `dfdxStraightNew`.
* Firefox 114 achieves approximately 13.1 million executions per second for the `sq` function using `dfdxStraightNew2`.

Note that these results are likely dependent on various factors, such as compiler optimizations, browser engine performance, and hardware capabilities.

Overall, this benchmark highlights the importance of considering both performance and readability/maintainability when writing mathematical functions, especially in specialized domains like fluid dynamics.

Related benchmarks:

dfdxstraight 2 (version: 0)

Comparing performance of: pow vs sq

Created: 2 years ago by: Guest

Jump to the latest result

pow

sq

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):