Introduction

Section author: Axel Huebl

Installing PIConGPU means installing C++ libraries that PIConGPU depends on and setting environment variables to find those dependencies. The first part is usually the job of a system administrator while the second part needs to be configured on the user-side.

Depending on your experience, role, computing environment and expectations for optimal hardware utilization, you have several ways to install and select PIConGPU’s dependencies. Choose your favorite install and environment management method below, young padavan, and follow the corresponding sections of the next chapters.

Before you Start

Important

For many HPC systems we already prepared and maintain an environment for you which will run out-of-the-box. See if yours is in the list so you can skip the installation completely!

Ways to Install

Spack

[Spack] is a flexible package manager that can build and organize software dependencies for you. It can be configured once for your hardware architecture to create optimally tuned binaries and provides modulefile support (e.g. [modules], [Lmod]). Those auto-build modules manage your environment variables and allow easy switching between versions, configurations and compilers.

Build from Source

You choose a supported C++ compiler and configure, compile and install all missing dependencies from source. You are responsible to manage the right versions and configurations. Performance will be ideal if architecture is chosen correctly (and/or if build directly on your hardware). You then set environment variables to find those installs.

Conda

We currently do not have an official conda install (yet). Due to pre-build binaries, performance will be sub-ideal and HPC cluster support (e.g. MPI) might be very limited. Useful for small desktop or single-node runs.

Nvidia-Docker

Not yet officially supported but we already provide a Dockerfile to get started. Performance might be sub-ideal if the image is not build for the specific local hardware again. Useful for small desktop or single-node runs. We are also working on Singularity images.

References

[Spack]T. Gamblin and contributors. A flexible package manager that supports multiple versions, configurations, platforms, and compilers, SC ‘15 Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2015), DOI:10.1145/2807591.2807623, https://github.com/LLNL/spack
[modules]J.L. Furlani, P.W. Osel. Abstract Yourself With Modules, Proceedings of the 10th USENIX conference on System administration (1996), http://modules.sourceforge.net
[Lmod]R. McLay and contributors. Lmod: An Environment Module System based on Lua, Reads TCL Modules, Supports a Software Hierarchy, https://github.com/TACC/Lmod
[nvidia-docker]Nvidia Corporation and contributors. Build and run Docker containers leveraging NVIDIA GPUs, https://github.com/NVIDIA/nvidia-docker