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C* C#; JavaScript; TypeScript; C++ AMP; Charm++; Cind; D programming language; Eiffel SCOOP (Simple Concurrent Object-Oriented Programming) Emerald; Fortran from the ISO Fortran 2003 standard; Java; Join Java - A Java-based language with features from the join-calculus. LabVIEW; ParaSail; Python [3] Ruby
The Chapel compiler is written in C and C++ . The backend (i.e. the optimizer) is LLVM, written in C++. Python 3.7 or newer is required for some optional components such Chapel’s test system and c2chapel, a tool to generate C bindings for Chapel. By default Chapel compiles to binary executables, but it can also compile to C code, and then ...
OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, [3] on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows.
[3] [4] Nuitka initially was designed to produce C++ code, but current versions produce C source code using only those features of C11 that are shared by C++03, enabling further compilation to a binary executable format by modern C and C++ compilers including gcc, clang, MinGW, or Microsoft Visual C++. It accepts Python code compatible with ...
The default can be overridden (e.g. in source code comment) to Python 3 (or 2) syntax. Since Python 3 syntax has changed in recent versions, Cython may not be up to date with the latest additions. Cython has "native support for most of the C++ language" and "compiles almost all existing Python code". [7] Cython 3.0.0 was released on 17 July ...
The ROSE compiler framework, developed at Lawrence Livermore National Laboratory (LLNL), is an open-source software compiler infrastructure to generate source-to-source analyzers and translators for multiple source languages including C (C89, C99, Unified Parallel C (UPC)), C++ (C++98, C++11), Fortran (77, 95, 2003), OpenMP, Java, Python, and PHP.
Concurrent computations may be executed in parallel, [3] [6] for example, by assigning each process to a separate processor or processor core, or distributing a computation across a network. The exact timing of when tasks in a concurrent system are executed depends on the scheduling , and tasks need not always be executed concurrently.
A skilled parallel programmer may take advantage of explicit parallelism to produce efficient code for a given target computation environment. However, programming with explicit parallelism is often difficult, especially for non-computing specialists, because of the extra work and skill involved in developing it.