Optimizing Array Summation with Loop Unrolling in Go

Loop unrolling is a compiler optimization technique that can improve program performance by reducing loop overhead. This challenge asks you to manually implement loop unrolling for a simple array summation function in Go. By performing loop unrolling, you'll aim to reduce the number of loop iterations, thereby decreasing branching and potentially improving instruction-level parallelism.

Problem Description

Your task is to create a Go function that calculates the sum of all elements in an integer slice. You will implement two versions of this function:

Standard Loop Version: A straightforward implementation using a traditional for loop.
Loop Unrolled Version: An optimized version where you manually "unroll" the loop to process multiple elements per iteration.

The goal is to demonstrate how loop unrolling can be applied and to observe potential performance differences.

Key Requirements:

Create a function SumSliceStandard(data []int) that sums elements using a standard for loop.
Create a function SumSliceUnrolled(data []int) that sums elements using loop unrolling. You should choose a reasonable unrolling factor (e.g., 4 or 8).
Both functions should produce identical results for the same input.
The unrolled version should handle cases where the slice length is not perfectly divisible by the unrolling factor.

Expected Behavior:

Given an input slice of integers, both functions should return the correct sum of all its elements.

Edge Cases to Consider:

Empty slices ([]int{}).
Slices with a single element.
Slices whose length is not a multiple of your chosen unrolling factor.

Examples

Example 1:

Input: data = []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
Output: SumSliceStandard(data) = 55
Output: SumSliceUnrolled(data) = 55
Explanation: Both functions correctly sum all elements in the slice.

Example 2:

Input: data = []int{10, 20, 30, 40}
Output: SumSliceStandard(data) = 100
Output: SumSliceUnrolled(data) = 100
Explanation: A slice with a length perfectly divisible by a common unrolling factor.

Example 3:

Input: data = []int{5, 10, 15, 20, 25}
Output: SumSliceStandard(data) = 75
Output: SumSliceUnrolled(data) = 75
Explanation: A slice whose length (5) is not a multiple of a potential unrolling factor (e.g., 4). The unrolled version must correctly handle the remaining elements.

Example 4:

Input: data = []int{}
Output: SumSliceStandard(data) = 0
Output: SumSliceUnrolled(data) = 0
Explanation: An empty slice should result in a sum of 0.

Constraints

The input data will always be a slice of int.
The slice can contain positive, negative, or zero values.
The maximum length of the slice will not exceed 1,000,000 elements.
The sum of the elements will fit within a standard int type in Go.
For performance comparison purposes, aim for an unrolling factor of at least 4.

Notes

The primary goal is correctness. Performance optimization through unrolling is secondary to ensuring both functions yield identical results.
Consider how you will handle the "tail" of the slice when its length is not perfectly divisible by your unrolling factor in the SumSliceUnrolled function. A common approach is to use a separate, smaller loop for these remaining elements.
While modern compilers often perform loop unrolling automatically, this exercise is about understanding the manual process.
You can use Go's built-in benchmarking tools (go test -bench=.) to compare the performance of your two implementations on larger datasets.