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LLVM can accept the IR from the GNU Compiler Collection (GCC) toolchain, allowing it to be used with a wide array of extant compiler front-ends written for that project. LLVM can also be built with gcc after version 7.5. [37] LLVM can also generate relocatable machine code at compile-time or link-time or even binary machine code at runtime.
The LLDB debugger is known to work on macOS, Linux, FreeBSD, NetBSD and Windows, [4] and supports i386, x86-64, and ARM instruction sets. [5] LLDB is the default debugger for Xcode 5 and later. Android Studio also uses LLDB for debug. [6] LLDB can be used from other IDEs, including Visual Studio Code, [7] C++Builder, [8] Eclipse, [9] and CLion ...
As with LLVM, it is free and open-source software under the Apache 2.0 software license. [ 5 ] [ 6 ] Its contributors include Apple , Microsoft , Google , ARM , Sony , Intel , and AMD . Clang 17, the latest major version of Clang as of October 2023, has full support for all published C++ standards up to C++17 , implements most features of C++20 ...
In 2005, Mingw-w64 was created by OneVision Software under cleanroom software engineering principles, since the original MinGW project was not prompt on updating its code base, including the inclusion of several key new APIs and also much needed 64-bit support.
The AMD Optimizing C/C++ Compiler (AOCC) is an optimizing C/C++ and Fortran compiler suite from AMD targeting 32-bit and 64-bit Linux platforms. [1] [2] It is a proprietary fork of LLVM + Clang with various additional patches to improve performance for AMD's Zen microarchitecture in Epyc, and Ryzen microprocessors.
Compiler Author Windows Unix-like Other OSs License type PTC ObjectAda: PTC, Inc. Yes: Yes: Yes: Proprietary: GCC GNAT: GNU Project: Yes: Yes: Yes: GPLv3+ GNAT LLVM ...
Android uses Bionic as its C library, and the LLVM libc++ as its C++ Standard Library. The NDK also includes a variety of other APIs: [ 9 ] zlib compression, OpenGL ES or Vulkan graphics, OpenSL ES audio, and various Android-specific APIs for things like logging, access to cameras, or accelerating neural networks .
MLIR (Multi-Level Intermediate Representation) is a unifying software framework for compiler development. [1] MLIR can make optimal use of a variety of computing platforms such as central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), Tensor Processing Units (TPUs), field-programmable gate arrays (FPGAs), artificial intelligence (AI) application ...