Add initial dev environment and Singularity example

.dockerignore

+.git/

.gitlab-ci.yml

+docker-build-tags:
+  image: docker:latest
+  stage: build
+  tags: 
+    - docker-machine
+  variables:
+    DOCKER_DRIVER: overlay2
+  services:
+    - docker:dind
+  before_script:
+    - docker login -u "$CI_REGISTRY_USER" -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY
+  script:
+    - docker build --pull -t "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG" .
+    - docker push "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG"
+  only:
+    - tags
+
+docker-build:
+  image: docker:latest
+  stage: build
+  tags: 
+    - docker-machine
+  variables:
+    DOCKER_DRIVER: overlay2
+  services:
+    - docker:dind
+  before_script:
+    - docker login -u "$CI_REGISTRY_USER" -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY
+  script:
+    - docker build --pull -t "$CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG" .
+    - docker push "$CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG"
+  except:
+    - tags

Dockerfile

+FROM centos:8
+
+# Nvidia HPC compiler SDK setup documentation
+# https://docs.nvidia.com/hpc-sdk/hpc-sdk-install-guide/index.html
+# https://docs.nvidia.com/hpc-sdk/eula/index.html
+# https://developer.nvidia.com/nvidia-hpc-sdk-download
+
+RUN yum update -y \
+# Install CentOS development tools and provide Nvidia compiler GCC dependencies
+# List of the following: optional, default, mandatory. Tells dnf which type of packages in groups will be installed when ‘groupinstall’ is called. Default is: default, mandatory. (https://dnf.readthedocs.io/en/latest/conf_ref.html)
+&& yum --setopt=group_package_types=mandatory group install "Development Tools" -y \
+# Add optional GCC Fortran compiler
+&& yum install gcc-gfortran -y \
+# Provide man page support
+&& yum install man-pages man-db man -y \
+# Clean-up package manager
+&& yum clean all
+
+# https://docs.nvidia.com/hpc-sdk/hpc-sdk-install-guide/index.html
+# Note: Any changes to your gcc compilers requires you to reinstall the HPC SDK.
+
+ARG NVIDIA_SDK_VERSION=nvhpc_2020_207_Linux_x86_64_cuda_multi
+ARG SDK_INSTALL_DIR=/nvidia/hpc_sdk
+
+# do: https://developer.download.nvidia.com/hpc-sdk/md5sum.txt
+
+# Install system dependencies
+RUN yum update -y \
+&& yum install wget -y \
+&& yum clean all
+
+# Install Nvidia compiler SDK
+RUN wget --quiet https://developer.download.nvidia.com/hpc-sdk/${NVIDIA_SDK_VERSION}.tar.gz \
+&& tar xpzf ${NVIDIA_SDK_VERSION}.tar.gz \
+&& rm -f ${NVIDIA_SDK_VERSION}.tar.gz \
+&& export NVHPC_SILENT=true && export NVHPC_INSTALL_DIR=$SDK_INSTALL_DIR && export NVHPC_INSTALL_TYPE=single \
+&& ${NVIDIA_SDK_VERSION}/install \
+# Clean-up obsolete files/directories
+&& rm -rf ${NVIDIA_SDK_VERSION}
+
+# Set-up Nvidia SDK environment settings
+
+ENV NVARCH=Linux_x86_64 \
+NVCOMPILERS=$SDK_INSTALL_DIR \
+MANPATH=$MANPATH:$NVCOMPILERS/$NVARCH/20.7/compilers/man \
+PATH=$NVCOMPILERS/$NVARCH/20.7/compilers/bin:$PATH \
+PATH=$NVCOMPILERS/$NVARCH/20.7/comm_libs/mpi/bin:$PATH \
+MANPATH=$MANPATH:$NVCOMPILERS/$NVARCH/20.7/comm_libs/mpi/man
+
+# Further useful stuff
+
+RUN yum update -y \
+&& yum install vim git -y \
+&& yum clean all
+

README.md

-# nvidia-compiler-sdk-container
+# Nvidia compiler SDK container
 
-C, C++ and FORTRAN compiler environment for GPU programming
\ No newline at end of file
+A ready-to-use C, C++ and FORTRAN compiler environment for GPU programming.
+
+```shell
+$ singularity shell --nv ${NVIDIA_IMAGE_PATH}
+$ git clone git@git.geomar.de:scientific_computing_infrastructure/nvidia-compiler-sdk-container.git
+$ cd nvidia-compiler-sdk-container/test_nvidia_compilers/
+$ bash run_tests.sh 
+ Test Passed
+ Test Passed
+```

build_env.sh

+
+docker build -t nvidia_sdk .
+docker save --output=nvidia_sdk.tar nvidia_sdk:latest
+singularity build nvidia_sdk.sif docker-archive://nvidia_sdk.tar
+

test_gnu_compilers/README.md

+# GNU compiler examples
+
+These should compile/execute without errors.
+
+## References
+
+* https://docs.nvidia.com/hpc-sdk/hpc-sdk-install-guide/index.html#install-linux-prep
+* https://en.wikibooks.org/wiki/Fortran/Fortran_examples#FORTRAN_77
+* https://en.wikibooks.org/wiki/Fortran/Fortran_examples#Fortran_90
+

test_gnu_compilers/hello.c

+#include <stdio.h>
+int main() {
+  printf("Hello, world!\n");
+  return 0;
+}

test_gnu_compilers/hello.cpp

+#include <iostream>
+int main() {
+  std::cout << "Hello, world!\n";
+  return 0;
+}

test_gnu_compilers/hello.f

+      PROGRAM HELLO
+       PRINT '(A)', 'Hello, world!'
+       STOP
+      END PROGRAM

test_gnu_compilers/hello.f90

+program hello
+ write(*,*) "Hello, world!"
+end program hello

test_gnu_compilers/run_tests.sh

+#!/bin/bash
+
+gcc -m64 -o hello_64_c hello.c
+g++ -m64 -o hello_64_cpp hello.cpp
+
+./hello_64_c
+./hello_64_cpp
+
+gfortran -o hello_f hello.f
+gfortran -o hello_f90 hello.f90
+
+./hello_f
+./hello_f90
+

test_nvidia_compilers/README.md

+# Nvidia compiler examples
+
+These should compile/execute without errors.
+The `*.cuf` executables only run correctly with a GPU device available.
+
+## References
+
+* Introduction to CUDA Fortran; https://on-demand.gputechconf.com/gtc/2013/presentations/S3050-Intro-to-CUDA-Fortran.pdf
+

test_nvidia_compilers/run_tests.sh

+#!/bin/bash
+
+nvfortran -o test_cpu_f90 test_cpu.f90
+./test_cpu_f90
+
+nvfortran -o test_gpu_cuf test_gpu.cuf
+./test_gpu_cuf
+

test_nvidia_compilers/test_cpu.f90

+program copyData
+! use cudafor
+ implicit none
+ integer, parameter :: n = 256
+ real :: a(n), b(n)
+! real, device :: a_d(n), b_d(n)
+ a = 1.0
+! a_d = a
+! b_d = a_d
+ b = a
+ if (all(a == b)) &
+ write(*,*) 'Test Passed'
+end program copyData

test_nvidia_compilers/test_gpu.cuf

+program copyData
+ use cudafor
+ implicit none
+ integer, parameter :: n = 256
+ real :: a(n), b(n)
+ real, device :: a_d(n), b_d(n)
+ a = 1.0
+ a_d = a
+ b_d = a_d
+ b = b_d
+ if (all(a == b)) &
+ write(*,*) 'Test Passed'
+end program copyData