Slices and Strides in Programming — A Complete, Intuitive, and C++-Focused Guide

slices_strides.md

#️⃣ Slices and Strides in Programming — A Complete, Intuitive, and C++-Focused Guide

Disclaimer: ChatGPT generated document.

In programming, two closely related concepts frequently appear when working with sequences, arrays, strings, buffers, tensors, or multidimensional data:

• Slice — a contiguous subset or “view” into a sequence
• Stride — the step size used when walking through memory or selecting elements

Both terms come from simple physical metaphors — one from cutting, the other from stepping. But their conceptual and practical importance runs very deep, especially in languages like C++, Python, Rust, Go, and data frameworks like NumPy, BLAS, or GPU kernels.

This article explains:

1. What slices are
2. What strides are
3. Why they are called that
4. How they are implemented in C++, Python, and systems programming
5. How they relate to memory, performance, and modern API design

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🍰 1. What Is a Slice?

A slice is a view into part of a larger sequence — usually zero-copy, non-owning, and defined by:

• A starting point (pointer/offset)
• A length (number of elements)

Conceptually:

“Give me the portion of the sequence from index start to end.”

Why the name “slice”?

Because you are literally taking a slice of a larger thing, like slicing:

• a cake
• a loaf of bread
• a log
• a pizza

A contiguous, nicely cut-out piece from the whole.

This metaphor works incredibly well because a slice is:

• contiguous
• bounded
• non-copying (you don't bake a new cake — you cut from the original)

Slices in different languages

Python

arr = [10, 11, 12, 13, 14]
part = arr[1:4]  # [11, 12, 13]

Rust

let arr = [10, 11, 12, 13, 14];
let part = &arr[1..4];  // slice: [11, 12, 13]

C++20: Slice as a Modern Non-Owning View (std::span)

In modern C++20, the canonical slice type is std::span<T>:

std::vector<int> v = {10, 11, 12, 13, 14};
std::span<int> slice(v.data() + 1, 3);   // [11, 12, 13]

A std::span stores:

• pointer to first element
• size (count of elements)

It does not own data, and it does not copy data.

It is the closest built-in abstraction C++ has to a “slice.”

Other slice-like abstractions in C++:

• std::string_view — slice of a string
• std::ranges::subrange — slice in ranges pipelines
• gsl::span — precursor to std::span
• Typical APIs using (ptr, length) pairs

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🏃 2. What Is a Stride?

A stride determines how far you step in memory between consecutive elements.

“Select every k-th element.”

“Advance the pointer by this number of bytes each time.”

Why the name "stride"?

Because it comes from walking:

• Your stride is the distance between your steps.

In memory terms:

A stride is the number of bytes to jump to reach the next element.

Examples:

• Stride = 1 element → contiguous
• Stride = 2 elements → every second element
• Stride = 4 bytes, 12 bytes, etc. → in matrix row/column traversal

Stride in Python

arr = [10, 11, 12, 13, 14, 15]
evens = arr[0:6:2]   # [10, 12, 14]   stride = 2

Stride in NumPy (very important)

NumPy arrays store strides in bytes:

For a 3×3 float32 matrix:

• stride[0] = 3 * 4 bytes = 12 → step to next row
• stride[1] = 4 bytes → step to next column

This enables reshaping, slicing, and transposing without copying memory.

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🔧 3. How Strides Work in C++ (Low-Level)

Unlike Python/NumPy, C++ does not automatically store “stride metadata,” but you implement strides manually using pointer arithmetic.

Example: iterate with stride 2

int arr[] = {10, 11, 12, 13, 14, 15};

for (int i = 0; i < 6; i += 2) {
    std::cout << arr[i] << ' ';
}

More explicit: pointer arithmetic

int* p = arr;  
int stride = 2;

for (int i = 0; i < 3; ++i)
    std::cout << *(p + i * stride) << " ";

This is exactly how BLAS/LAPACK, Eigen, and GPU kernels implement strided memory access.

Strides in C++ libraries

• Eigen uses InnerStride and OuterStride templates.
• MDSPAN (proposed for C++23+ and now available in some compilers) lets you define explicit strides for multidimensional arrays.
• BLAS/LAPACK C APIs always accept stride parameters (incx, incy) for vector operations.

Example BLAS call:

cblas_saxpy(n, alpha, x, incx, y, incy);

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🪚 4. Combining Slice + Stride

The two concepts combine into:

A strided slice — a view that selects a contiguous portion using steps.

Examples:

Python

arr = [0, 1, 2, 3, 4, 5, 6, 7, 8]
slice_with_stride = arr[2:9:3]  # [2, 5, 8]

C++20 without built-in support

C++ has no built-in “strided span,” but you can implement one:

template<typename T>
struct strided_span {
    T* data;
    size_t count;
    size_t stride; // in elements

    T& operator[](size_t i) const { return data[i * stride]; }
};

This is essentially how:

• Eigen slices work
• NumPy views work internally
• Many HPC codebases manage multidimensional memory
• GPU kernels process tiled data

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🧠 5. Why Slices and Strides Matter (Performance & Design)

📌 Performance

• Slicing avoids copies → excellent for zero-cost APIs
• Striding allows accessing patterns efficiently
• Better cache locality if stride = 1
• Potential SIMD-friendly access

📌 Memory layout

Strides describe how multidimensional arrays lie in memory:

• Row-major (stride_col = sizeof(T))
• Column-major (Fortran, BLAS)
• Tiled/block layouts (GPU optimization)

📌 API design

C++20 pushes toward:

• non-owning views (span, string_view)
• boundaries/enforced safety
• better composability
• zero-overhead abstractions

Slices and strides are foundational to this philosophy.

📌 Zero-copy data manipulation

Slices + strides = many transformations “for free”:

• subarrays
• windows
• downsampling
• flattening
• transposing (via strides)
• reshaping

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📝 6. Summary (Cheat Sheet)

Concept	Meaning	Example	C++ Equivalent
Slice	contiguous piece of a sequence	arr[2:5]	std::span, std::string_view
Stride	step size between selected elements	arr[::2]	pointer arithmetic, BLAS incx/incy
Strided Slice	slice with a step	arr[1:8:3]	custom strided-span, Eigen Map

Slice = “cut a piece out of the sequence.” Stride = “walk through data in fixed steps.”

Together, they give expressive, memory-efficient control over sequences.

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Want more?

I can extend the article with:

• How NumPy calculates strides internally
• How mdspan gives C++ a NumPy-like memory layout
• SIMD vectorization implications
• How to design your own C++ slicing API for your libraries
• How slicing and strides interact with cache lines
• How striding affects performance on modern CPUs

Just tell me what you'd like to dive into next!