steindev

1. Install picongpu conda environment and activate it
2. Install clion sudo snap install clion --classic
3. Start clion, choose 'Open' and set Toolchain:
   • CMake: .../anaconda3/envs/picongpu/bin/cmake
   • Build Tool: .../anaconda3/envs/picongpu/bin/make
   • C Compiler: .../anaconda3/envs/picongpu/bin/x86_64-conda-linux-gnu-cc
   • C++ Compiler: .../anaconda3/envs/picongpu/bin/x86_64-conda-linux-gnu-c++
4. CMake Setting:
   • CMake options: -DCMAKE_CXX_FLAGS=-g -DCMAKE_INSTALL_PREFIX=. -DPIC_EXTENSION_PATH=/home/.../src/picongpu/share/picongpu/examples/KelvinHelmholtz -Dalpaka_ACC_CPU_B_OMP2_T_SEQ_ENABLE=ON -DCUPLA_STREAM_ASYNC_ENABLE=OFF -DPMACC_CPU_ARCH="native"

• Build directory: /home/.../picInput/clion_build

steindev

## Install a kernel for the picongpu analysis environment
## 
## Before runnign this script, see JSC documentation at
##     (https://docs.jupyter-jsc.fz-juelich.de/github/FZJ-JSC/jupyter-jsc-notebooks/blob/documentation/index.ipynb) -> 'Install kernel with conda'
##
## The steps performed here are only the lsat ones after installing an own conda environment.
##


CONDA_TARGET_DIR=/p/project/<PROJECTNAME>/<USERNAME>/miniconda3

steindev

## Installing the environment
# * Download and install anaconda or miniconda
# * Activate the base environment
#   `conda activate` (which maybe preceeded by `source ~/.bashrc`)
# * Install
#   `conda env create --file picongpu.yml`
#
# Alternatively, mamba could be used instead of conda which is supposed
# to be faster.
# In the base environment

steindev

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger
#
# last updated: 2022-03-02

PIC_BRANCH="dev"

# get PIConGPU profile
if [ ! -f "$PIC_PROFILE" ]; then

steindev

#!/bin/bash
#
# Script to transfer data from Juelich Supercomputing Center to HZDR.
# Call with
#     > screen # open a screen session first to be able to logout from the
#              # data mover system
#     > exec ssh-agent bash # prepare shell to add ssh key passphrase to ssh-agent
#                           # in order to not type it all the time
#     > ssh-add ~/.ssh/id_ed25519 # add ssh key passphrase to ssh-agent 
#     > xargs -a dirs.list -n 1 -P 5 ~/bin/data-transfer_judac.sh | tee transfer.out

steindev

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger
#
# last updated: 2021-08-18

# It is advisable to compile libraries on an interactive node
# > srun -p gpu2-interactive --gres=gpu:0 --ntasks=1 --pty --mem=8G -t 2:00:00 bash  # get resources on a node
# > ./PIConGPU_0.6.0-dev_taurus-tud.sh | tee lib_inst.out 2>lib_inst.err  # compile libraries

steindev

# See https://github.com/ComputationalRadiationPhysics/picongpu/issues/3530
# and https://github.com/ComputationalRadiationPhysics/picongpu/issues/3506

TBG_ADIOS2_configuration="'{                             \
   \"adios2\": {                                         \
       \"dataset\": {                                    \
           \"operators\": [ {                            \
               \"type\": \"blosc\"                       \
               , \"parameters\": {                       \
                   \"clevel\": \"1\"                     \

steindev

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger
#
# last updated: 2021-05-26

PIC_BRANCH="dev"

# get PIConGPU profile
if [ ! -f "$PIC_PROFILE" ]; then

steindev

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger, Anton Lebedev
#
# last updated: 2022-06-13

PIC_BRANCH="dev"

# get PIConGPU profile
if [ ! -f "$PIC_PROFILE" ]; then

steindev

## Installing the environment
# * Download and install anaconda or miniconda
# * Activate the base environment
#   `conda activate` (which maybe preceeded by `source ~/.bashrc`)
# * Install
#   `conda env create --file picongpu-analysis-environment.yml`
#
# Alternatively, mamba could be used instead of conda which is supposed
# to be faster.
# In the base environment