Benchmark: Max and Min values

Script Preparation code:

var data = []
   
for (var x = 1; x <= 100000; x++) {
  data.push({ x: x, y: Math.floor(Math.random() * (1000000)) })
}

Tests:

Reduce
function getMinY() { return data.reduce((min, p) => p.y < min ? p.y : min, data[0].y); } function getMaxY() { return data.reduce((max, p) => p.y > max ? p.y : max, data[0].y); } const min = getMinY(); const max = getMaxY();
For
function getMinY(){ var minY = data[0].y; for (var i = 1; i < data.length; i++) { if (data[i].y < minY) { minY = data[i].y; } } return minY; } function getMaxY(){ var maxY = data[0].y; for (var i = 1; i < data.length; i++) { if (data[i].y > maxY) { maxY = data[i].y; } } return maxY; } var min = getMinY(); var max = getMaxY();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
For

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 break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark measures the performance of two different approaches for finding the minimum and maximum values in an array of random numbers. The test cases are:

1. "Reduce": This approach uses the `reduce()` method to find the minimum and maximum values in the array.
2. "For": This approach uses a traditional `for` loop to iterate through the array and find the minimum and maximum values.

**Script Preparation Code**

The script preparation code generates an array of 100,000 random numbers with values between 0 and 1 million. The purpose of this code is to create a large dataset for the benchmark to test its performance.

**Benchmark Definition**

The benchmark definition provides two functions:

`getMinY()` and `getMaxY()`

These functions are identical in both test cases, but they differ in their implementation approach.

**"Reduce" Test Case**

This test case uses the `reduce()` method to find the minimum and maximum values in the array. The `reduce()` method applies a callback function to each element of the array, reducing it to a single value.

**Pros:**

* Concise and efficient
* Works well with large datasets

**Cons:**

* May be slower than traditional loops for very large datasets due to the overhead of function calls
* Limited control over iteration order and bounds checking

**"For" Test Case**

This test case uses a traditional `for` loop to iterate through the array and find the minimum and maximum values.

**Pros:**

* Provides explicit control over iteration order and bounds checking
* Can be faster than `reduce()` for very large datasets due to reduced overhead

**Cons:**

* More verbose and error-prone
* May have slower performance due to the overhead of manual loop management

**Library Used**

In this benchmark, no libraries are explicitly mentioned. However, it's worth noting that some JavaScript engines may optimize or implement certain functions (like `reduce()`) using native code, which could affect the benchmark results.

**Special JS Feature/Syntax**

There is no special JavaScript feature or syntax used in this benchmark. It only relies on standard JavaScript features and methods.

**Alternatives**

If you wanted to test alternative approaches for finding minimum and maximum values in an array, some possible options could include:

* Using `Math.min()` and `Math.max()` functions (which are likely to be faster than the `reduce()` method)
* Implementing a custom binary search algorithm
* Using parallel processing or multi-threading techniques to leverage multiple CPU cores

Keep in mind that each alternative approach would require significant changes to the benchmark code, and its performance impact may vary depending on the specific use case and system configuration.

Related benchmarks:

Max and Min values (version: 0)

Comparing performance of: Reduce vs For

Created: 8 years ago by: Guest

Jump to the latest result

Reduce

For

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):