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As of August 5, 2020, the "PGI Compilers and Tools" technology is a part of the NVIDIA HPC SDK product, available as a free download from NVIDIA.
| Invocations | Usage |
|---|---|
| nvc | Compile C source files (C11 compiler. It supports GPU programming with OpenACC, and supports multicore CPU programming with OpenACC and OpenMP) |
| nvc++ | Compile C++ source files (C++17 compiler. It supports GPU programming with C++17 parallel algorithms (pSTL) and OpenACC, and supports multicore CPU programming with OpenACC and OpenMP) |
| nvfortran | Compile FORTRAN source files (supports ISO Fortran 2003 and many features of ISO Fortran 2008. It supports GPU programming with CUDA Fortran and OpenACC, and supports multicore CPU programming with OpenACC and OpenMP) |
| nvcc | CUDA C and CUDA C++ compiler driver for NVIDIA GPUs |
For legacy reasons, the nVIDIA nvhpc suite also offers the PGI C, C++, and Fortran compilers with their original names:
| Invocations | Usage |
|---|---|
| pgcc | Compile C source files. |
| pgc++ | Compile C++ source files. |
| pgf77 | Compile FORTRAN 77 source files |
| pgf90 | Compile FORTRAN 90 source files |
| pgf95 | Compile FORTRAN 95 source files |
To enable CUDA C++ or CUDA Fortran, and link with the CUDA runtime libraries, use the -cuda option (-Mcuda is deprecated). Use the -gpu option to tailor the compilation of target accelerator regions.
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The OpenMP parallelization is enabled by the -mp compiler option. The GPU offload via OpenMP is enabled by the -mp=gpu option.
GNU compiler collection
The gnu compilers are always available. GCC version 8.5.0 is available without the need to load any gcc module. In the module environment you can find more recent version though.
The name of the GNU compilers are:
- g77: Fortran77 compiler
- gfortran: Fortran95 compiler
- gcc: C compiler
- g++: C++ compiler
The documentation can be obtained with the man command after loading the gnu module:
> man gfortan
> man gcc
CUDA
Compute Unified Device Architecture is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords. We refer to the NVIDIA CUDA Parallel Computing Platform documentation.
Debugger and Profilers
It will be available soon.