Snippet

N-Dimensional Index Linearization for High-Performance Arrays

Expert pattern for handling multi-dimensional data in a single flat memory block. Linearization improves cache locality and performance compared to nested pointers (arrays of arrays), which cause pointer chasing.

snippet.cpp

cpp

template <typename T, size_t W, size_t H, size_t D>
class Tensor {
    T data[W * H * D];
public:
    T& operator()(size_t x, size_t y, size_t z) {
        // Index linearization formula: z * (Width * Height) + y * Width + x
        return data[z * (W * H) + y * W + x];
    }
    constexpr size_t size() const { return W * H * D; }
};

Breakdown

T data[W * H * D];

Allocates a single contiguous block of memory for all dimensions.

data[z * (W * H) + y * W + x]

Calculates the exact 1D offset from 3D coordinates (x, y, z).

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N-Dimensional Index Linearization for High-Performance Arrays

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