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.
Ways to Install¶
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 can be near-ideal if architecture is choosen correctly (and/or if build directly on your hardware). You then set environment variables to find those installs.
[Spack] is a flexible package manager for HPC systems that can organize versions and 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.
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.
Use your package manager to install drivers and core dependencies, e.g. via apt-get install as far as possible. Build further dependencies from source.
Alternately, use [Spack] for all dependencies.
For single nodes, essentially the same as working via SSH on any other machine. We did not investigate deeper into multi-node cloud setups yet.
|[Spack]||(1, 2, 3, 4, 5, 6, 7) 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]||(1, 2) 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]||(1, 2, 3) 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]||(1, 2, 3) Nvidia Corporation and contributors. Build and run Docker containers leveraging NVIDIA GPUs, https://github.com/NVIDIA/nvidia-docker|