In C++, serialization and deserialization of the `std::mdspan` (multi-dimensional span) can be achieved by converting the data it references into a format suitable for storage or transmission, such as JSON or binary. Below, we'll explore an example of how to serialize and deserialize a `std::mdspan` using a JSON-like approach.
std::mdspan, serialization, deserialization, C++17, C++23, multi-dimensional arrays, JSON, binary serialization
This document demonstrates how to serialize and deserialize std::mdspan in C++, making it easier to store and retrieve multi-dimensional array data.
// Example serialization and deserialization of std::mdspan
#include
#include
#include
#include // For JSON handling
using json = nlohmann::json;
// Utility function to serialize std::mdspan
template
json serialize_mdspan(std::mdspan> md)
{
json j;
// Create an array to hold the data
for (std::size_t i = 0; i < md.extent(0); ++i)
{
std::vector row(md.extent(1));
for (std::size_t j = 0; j < md.extent(1); ++j)
{
row[j] = md(i, j);
}
j.push_back(row);
}
return j;
}
// Utility function to deserialize std::mdspan
template
void deserialize_mdspan(json j, std::mdspan> md)
{
for (std::size_t i = 0; i < md.extent(0); ++i)
{
for (std::size_t j = 0; j < md.extent(1); ++j)
{
md(i, j) = j[i]; // Modify with actual data mapping
}
}
}
int main()
{
// Define a 2D array and wrap it into mdspan
std::array<:array>, 2> data = { { {1, 2, 3}, {4, 5, 6} } };
auto md = std::mdspan>(data.data(), 2, 3);
// Serialize the mdspan
json j = serialize_mdspan(md);
std::cout << "Serialized mdspan: " << j.dump() << std::endl;
// Deserialize the json back to mdspan (assuming proper index mapping)
deserialize_mdspan(j, md);
return 0;
}
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?