JNA is a foreign function interface and native memory mapper that allows Java applications to call functions in shared native libraries without writing custom JNI wrapper code. It serves as a JNI alternative framework for invoking C functions and mapping native data structures using high-level interfaces. The library distinguishes itself through its ability to translate Java objects and primitives into C-style structs, unions, and pointers for cross-platform memory exchange. On Windows, it provides a dedicated toolkit for interacting with Component Object Model objects using both early and la
JavaCPP is a JNI C++ bridge and foreign function interface that provides a binding generator, a native library loader, and a native memory manager. It automates the creation of Java wrappers around native C++ headers and shared libraries to enable interoperability between the two languages. The project features a C++ binding generator that parses header files to automatically produce the necessary glue code and interfaces for calling native functions. It includes a native library loader that locates and extracts platform-specific binaries from the classpath into a local cache for runtime exec
This project provides a header-only C++ wrapper for the Node-API, serving as a framework for building high-performance native addons for Node.js. It acts as a bridge between C++ and JavaScript, offering an object-oriented interface that simplifies the creation of compiled extensions while managing the complexities of the language boundary. The library distinguishes itself by providing type-safe abstractions for data marshalling and memory management, ensuring that native and script-side objects are tracked and reclaimed correctly. It includes mechanisms for coordinating asynchronous tasks bet
Torch7 is a scientific computing environment and tensor computation library used for deep learning research and numerical analysis. It functions as a Lua-based framework for training neural networks and learning agents, providing a toolkit for implementing architectures and training through reinforcement learning algorithms. The project is distinguished by its tight integration with C, utilizing a binding layer to map high-level scripting to low-level C structures for direct memory access. It supports hardware-accelerated computation by offloading linear algebra and convolution operations to
