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Compiling applications in Puhti

General instructions

  • Whenever possible, use the local disk on the login node for compiling software.
    • Compiling on the local disk is much faster and shifts load from the shared file system.
    • The local disk is cleaned frequently, so please move your files elsewhere after compiling.

Building CPU applications


Intel reorganized their compiler suites and names of Intel compilers have changed following the Red Hat Enterprise Linux 8 (RHEL8) update on Puhti. In addition, Intel changed the underlying technology of their compilers and renamed the old compilers as Intel Compilers Classic.

C/C++ and Fortran applications can be built with Intel or GNU compiler suites. The compiler suite is selected via the Modules system, i.e.

# New Intel compilers 
module load intel-oneapi-compilers


# Old Intel compilers
module load intel-oneapi-compilers-classic


module load gcc

Different applications function better with different suites, so the selection needs to be done on a case-by-case basis.

The actual compiler commands for building a serial application with these suites:

Compiler suite C C++ Fortran
Intel, new icx icpx ifx
Intel, classic icc icpc ifort
GNU gcc g++ gfortran

Intel and GNU compilers use different compiler options. The recommended basic optimization flags are listed in the table below. It is recommended to start from the safe level and then move up to intermediate or even aggressive, while making sure the results are correct and the program's performance has improved.

Optimisation level Intel GNU
Safe -O2 -xHost -fp-model precise -O2 -march=native
Intermediate -O2 -xHost -O3 -march=native
Aggressive -O3 -xHost -fp-model fast=2 -no-prec-div -fimf-use-svml=true -qopt-zmm-usage=high -O3 -march=native -ffast-math -funroll-loops -mprefer-vector-width=512

A detailed list of options for the Intel and GNU compilers can be found on the man pages (man icc/ifort, man gcc/gfortran when the corresponding programming environment is loaded, or in the compiler manuals (see the links above).

Please note that some flags, for example -no-prec-div and -qopt-zmm-usage, are currently supported only by the intel classic compilers (icc/icpx/ifort). More information about the current and planned flags support for the intel compilers can be checked with icx -qnextgen-diag or in the manuals.

Also, not all applications benefit from the AVX-512 vector set (-xHost or -march=native). It may be a good idea to also test AVX2 (-xCORE-AVX2 or -mavx2) and compare the performance.

List all available versions of the compiler suites:

module spider intel-oneapi-compilers
module spider gcc

Building GPU Applications

GPU support in Puhti is provided through NVIDIA compilers:

  • The nvc compiler is a C11 compiler that supports OpenACC for NVIDIA GPUs, and OpenACC and OpenMP for multicore CPUs.

  • The nvc++ compiler is a C++17 compiler that supports GPU programming with C++17 parallel algorithms, OpenACC, and OpenMP offloading on NVIDIA GPUs. However, it does not currently support C++ CUDA codes.

  • The nvcc compiler is the CUDA C and CUDA C++ compiler driver for NVIDIA GPUs.

  • The nvfortran compiler is the CUDA Fortran compiler driver for NVIDIA GPUs, supporting both OpenACC and multicore processing for OpenACC and OpenMP.

Specific instructions on how to load and use these compilers are provided in the following sections.


The CUDA compiler (nvcc) takes care of compiling the CUDA code for the target GPU device and passing on the rest to a non-CUDA compiler (i.e. gcc). For example, to load the CUDA 11.7 environment together with the GNU compiler:

module load gcc/11.3.0 cuda/11.7.0

To generate code for a given target device, tell the CUDA compiler what compute capability the target device supports. On Puhti, the GPUs (Volta V100) support compute capability 7.0. Specify this using -gencode arch=compute_70,code=sm_70.

For example, compiling a CUDA kernel ( on Puhti:

nvcc -gencode arch=compute_70,code=sm_70

In principle, it is also possible to target multiple GPU architectures by repeating -gencode multiple times for different compute capabilities. However, this is not necessary on Puhti, since there is only one type of GPU.



OpenACC support is provided through the NVIDIA nvc and nvc++ compilers. However, it is important to note that the support can be somewhat limited and may lack certain functionalities, such as MPI parallelization. For additional information about OpenACC support, the CSC service desk should be contacted.

The compilers can be accessed through the NVIDIA HPC SDK module:

module load .unsupported
module load nvhpc/22.7

Enabling OpenACC support requires providing the -acc flag to the compiler. For Fortran codes, this can be achieved as follows:

nvfortran -acc example.F90 -gpu=cc70

For information about what the compiler actually does with the OpenACC directives, use -Minfo=all.

Building MPI applications

There are currently two MPI environments available: openmpi and intel-oneapi-mpi. The default is openmpi, which is also recommended to begin with.

If openmpi is incompatible with your application or delivers insufficient performance, please try another environment. The MPI environments can be used via module load, i.e.

module load openmpi

When building MPI applications, use mpixxx compiler wrappers that differ depending on the compiler suite and the MPI environment:

Compiler suite openmpi intel-oneapi-mpi
Intel mpifort, mpicc, mpicxx mpiifort, mpiicc, mpiicpc
GNU mpif90, mpicc, mpicxx incompatible

Building OpenMP and hybrid applications

Additional compiler and linker flags are needed when building OpenMP or MPI/OpenMP hybrid applications:

Compiler suite OpenMP flag
Intel -qopenmp
GNU -fopenmp

Building software using Spack

Spack is a flexible package manager that can be used to install software on supercomputers and Linux and macOS systems. The basic module tree including compilers, MPI libraries and many of the available software on CSC supercomputers have been installed using Spack.

CSC provides a module spack/v0.18-user on Puhti that can be used by users to build software on top of the available compilers and libraries using Spack. It is also possible to install different customized versions of packages available in the module tree for special use cases. See here for a short tutorial on how to install software on CSC supercomputers using Spack.

Last update: August 23, 2023