Benchmark: Data Processing Benchmark

Tests:

Optimized
const randomArray = Array.from({length: 1000}, () => Math.random()); const squareArr = (arr, len) => { const array = [] for (let i = 0; i < len; i++) { array.push(arr[i] * arr[i]) } return array } const sumArr = (arr, len) => { let total = 0 for (let i = 0; i < len; i++) { total += arr[i] } return total } const getMean = (arr, len) => sumArr(arr, len) / len const calculateRMS = (arr, len) => Math.sqrt(getMean(squareArr(arr, len), len)) calculateRMS(randomArray)
Original
const randomArray = Array.from({length: 1000}, () => Math.random()); const sumRdcr = (a, c) => a + c const getSum = (a) => a.reduce(sumRdcr, 0) const calculateRMS = function (arr) { const squares = arr.map((val) => val * val) const mean = getSum(squares) / arr.length return Math.sqrt(mean) } calculateRMS(randomArray)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Optimized
Original

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided benchmark compares two approaches to calculating the Root Mean Squared (RMS) value of an array of numbers. The first approach, "Optimized," is implemented in JavaScript using the `Array.from()` method and a custom `squareArr` function. The second approach, "Original," uses the `reduce()` method and a custom `sumRdcr` function.

**Test Case: Optimized**

In the optimized implementation:

*   The `randomArray` is generated using `Array.from()`, which creates a new array with 1000 random numbers.
*   The `squareArr` function takes an array `arr` and its length `len` as input, calculates the square of each element in the array, and returns the resulting array.
*   The `sumArr` function calculates the sum of all elements in the array using a simple loop.
*   The `getMean` function calculates the mean (average) of the array by dividing the sum by the length of the array.
*   Finally, the `calculateRMS` function uses the `squareArr` and `sumArr` functions to calculate the RMS value.

**Test Case: Original**

In the original implementation:

*   The same `randomArray` is generated as in the optimized implementation.
*   However, instead of using a custom `squareArr` function, it directly multiplies each element by itself using arithmetic operations (`val * val`).
*   Instead of using a custom `sumRdcr` function, it uses the `reduce()` method with the `sumRdcr` function as its callback.
*   The `calculateRMS` function remains similar to the optimized implementation.

**Options Compared**

The two implementations are compared in terms of execution speed. The "Optimized" implementation is expected to be faster than the "Original" implementation because it uses a more efficient way to calculate the square of each element and avoids unnecessary overhead.

**Pros and Cons:**

*   Optimized Implementation:
    *   Pros:
        +   Faster execution time due to reduced overhead.
        +   Easier to read and maintain, as it follows standard JavaScript array methods.
    *   Cons:
        +   May be less efficient for very large arrays, as `Array.from()` can create a new array object.
*   Original Implementation:
    +   Pros:
        - More efficient for very large arrays, as it avoids creating an intermediate array.
    *   Cons:
        +   Less readable and maintainable due to its custom implementation.

**Library and Syntax**

There are no external libraries used in this benchmark. However, the `reduce()` method is a built-in JavaScript method that reduces an array to a single value by applying a callback function to each element.

**Special JS Features or Syntax**

None of the implementations use any special JavaScript features or syntax beyond what is standard for JavaScript.

**Alternatives**

If you wanted to measure the execution speed of this benchmark, you could use other tools like:

*   Node.js `--measure-performance` flag
*   Browser's DevTools (e.g., Chrome DevTools)
*   External performance measurement libraries like `perf_hooks`

However, since MeasureThat.net already provides a pre-configured environment for running JavaScript microbenchmarks, it is likely the most convenient and efficient option.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36 Edg/90.0.818.66

Browser/OS: Chrome 90 on Windows

View result in a separate tab

Test name	Executions per second
Optimized	3360.3 Ops/sec
Original	3165.9 Ops/sec

Related benchmarks:

Data Processing Benchmark (version: 0)

Naive vs Optimized implementations

Comparing performance of: Optimized vs Original

Created: 3 years ago by: Registered User

Jump to the latest result

Optimized

Original

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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