Knowledge Base Resources
Contributed by cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators), these resources are shared through the ConnectCI community platform. Add resources you find helpful!
OpenMP Tutorial
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OpenMP (Open Multi-Processing) is an API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
Setting up PyFR flow solver on clusters
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These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation.
Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated.
1. Loading Modules
The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules:
module load foss/2022b
module load libffi/3.4.4
module load OpenSSL/1.1.1k
module load METIS/5.1.0
module load HDF5/1.13.1
2. Python Installation from Source
Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python:
cd $INSTALL/Python-3.11.1/
./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2
make clean; make -j20; make install;
3. Virtual Environment Setup
A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using:
pip3.11 install virtualenv
python3.11 -m venv pyfr-venv
. pyfr-venv/bin/activate
4. Install PyFR Dependencies
Several Python packages are required for PyFR. Install these packages using the following commands:
pip3 install --upgrade pip
pip3 install --no-cache-dir wheel
pip3 install --no-cache-dir botorch pandas matplotlib pyfr
pip3 uninstall -y pyfr
5. Install PyFR from Source
Finally, navigate to the directory containing the PyFR source code, and then install PyFR:
cd /scratch/user/sambit98/github/PyFR/
python3 setup.py develop
Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.
Examples of code using JSON nlohmann header only Library for C++
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This code showcases how to work with the header-only nlohmann JSON library for C++. In order to compile, change the extensions from json_test.txt to json_test.cpp and test.txt to test.json. You must also download the header files from https://github.com/nlohmann/json. Complilation instructions are at the bottom of json_test. This code is very helpful for creating config files, for example.
CUDA Toolkit Documentation
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NVIDIA CUDA Toolkit Documentation: If you are working with GPUs in HPC, the NVIDIA CUDA Toolkit is essential. You can access the CUDA Toolkit documentation, including programming guides and API references, at this provided website
Header-only C++ JSON library
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JSON is a lightweight format for storing and transporting data, for example in a config file. This library is header-only, and has easy-to-read documentation. It is a C++ library.
MATLAB with other Programming Languages
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MATLAB is a really useful tool for data analysis among other computational work. This tutorial takes you through using MATLAB with other programming languages including C, C++, Fortran, Java, and Python.
