| OS / Compiler | GCC 13 | GCC 14 | GCC 15 | Clang 14 | Clang 15 | Clang 17 |
|---|---|---|---|---|---|---|
| Ubuntu 22.04 |  |  |  |  |  |  |
| Ubuntu 24.04 |  |  |  |  |  |  |
| macOS |  |  |  |  |  |  |
| Windows |  |  |  |  |  |  |
This source code transformation tool simplifies the otherwise time consuming process of generating the shim code for HDF5 Compound datatypes by building the AST of a given TU translation unit, and identifying all POD datatypes referenced from H5CPP operators/functions. The result is a seamless persistence much similar to python, java or other reflection based languages.
The following excerpt shows the mechanism, how vec variable is marked by h5::write operator. When h5cpp tool is invoked it builds the full AST of the translation unit, finds the referenced types, then in topological order generates HDF5 COMPOUND datatype descriptors. The generated file has include guards, and meant to be used with H5CPP template library. POD struct types may be arbitrary deep, embedded in POD C like arrays, and may be referenced from STL containers. Currently stl::vector is supported, but in time full support will be provided.
sudo apt install build-essential cmake cmake -DCMAKE_BUILD_TYPE=Release -S . -B build cmake --build build --parallel sudo cmake --install build... std::vector<sn::example::Record> vec = h5::utils::get_test_data<sn::example::Record>(20); // mark vec with an h5:: operator and delegate // the details to h5cpp compiler h5::write(fd, "orm/partial/vector one_shot", vec ); ... // some include files with complex POD types, embedded in arbitrary name space namespace sn { namespace typecheck { struct Record { /*the types with direct mapping to HDF5*/ char _char; unsigned char _uchar; short _short; unsigned short _ushort; int _int; unsigned int _uint; long _long; unsigned long _ulong; long long int _llong; unsigned long long _ullong; float _float; double _double; long double _ldouble; bool _bool; // wide characters are not supported in HDF5 // wchar_t _wchar; char16_t _wchar16; char32_t _wchar32; }; } namespace other { struct Record { // POD struct with nested namespace MyUInt idx; // typedef type MyUInt aa; // typedef type double field_02[3]; // const array mapped typecheck::Record field_03[4]; // }; } namespace example { struct Record { // POD struct with nested namespace MyUInt idx; // typedef type float field_02[7]; // const array mapped sn::other::Record field_03[5]; // embedded Record sn::other::Record field_04[5]; // must be optimized out, same as previous other::Record field_05[3][8]; // array of arrays }; } namespace not_supported_yet { // NON POD: not supported in phase 1 // C++ Class -> PODstruct -> persistence[ HDF5 | ??? ] -> PODstruct -> C++ Class struct Container { double idx; // std::string field_05; // c++ object makes it non-POD std::vector<example::Record> field_02; // ditto }; } /* BEGIN IGNORED STRUCT */ // these structs are not referenced with h5::read|h5::write|h5::create operators // hence compiler should ignore them. struct IgnoredRecord { signed long int idx; float field_0n; }; /* END IGNORED STRUCTS */python3 -m venv .venv # Create a virtual env (Python 3.10+ recommended) source .venv/bin/activate # Activate: Linux / macOS # On Windows use: .venv\Scripts\activate pip install --upgrade pip # Upgrade pip pip install mkdocs-material # Install MkDocs + Material theme pip install python-frontmatter jinja2 python-dateutil pyyaml # Extra depssource .venv/bin/activate mkdocs serve --dev-addr=127.0.0.1:9000 # Live preview at http://127.0.0.1:9000 mkdocs build -v # Build static site locally