How to Install HPC Software and Libraries on a Cluster

Installing HPC software and libraries effectively is crucial for maximizing the performance and capability of your HPC cluster. Here's a step-by-step guide:

Step 1: Prepare the Cluster

• Ensure all nodes have a compatible Linux distribution installed (e.g., CentOS, Rocky Linux, Ubuntu).
• Synchronize system time (e.g., using NTP) across all nodes.

Step 2: Set Up Environment Management

• Install environment modules software (e.g., Lmod, Environment Modules).
• sudo yum install environment-modules
• Configure module files for easy switching and management of software environments.

Step 3: Install MPI Libraries

• Choose an MPI library like OpenMPI or MPICH.
• sudo yum install openmpi openmpi-devel
• Verify installation by checking the version:
• mpirun --version

Step 4: Install Job Scheduling Software

• Select a scheduler like Slurm, PBS, or HTCondor.
• sudo yum install slurm slurm-devel
• Configure scheduler settings (partitions, queues, resources).

Step 5: Install HPC Libraries and Compilers

• Install common libraries like BLAS, LAPACK, FFTW, and compilers such as GCC or Intel compilers.
• sudo yum install blas lapack fftw gcc gcc-c++ gcc-gfortran
• For Intel compilers or libraries, download and install from the official Intel website.

Step 6: Application-Specific Software

• Install software specific to your workloads (e.g., ANSYS, GROMACS, OpenFOAM).
• Use module files for different software versions to manage conflicts.

Step 7: Verification and Benchmarking

• Test installations by running sample jobs and verifying functionality.
• Benchmark with applications relevant to your workloads (Linpack, HPCG, or STREAM).

Step 8: Documentation and Training

• Document all installed software, modules, and usage guidelines for end-users.
• Offer training or reference materials for users to effectively leverage the HPC environment.

Following these steps will ensure your HPC software and libraries are correctly installed and optimized, supporting efficient and productive cluster usage.