Solving the Dependency Puzzle: Understanding Spack Concretization
Learn how Spack's concretization process uses SAT solvers to resolve complex HPC dependency trees and how to use 'spack spec' to verify your software stack.
Loading…
ReadMeFeed / Community knowledge
#hpc
Real questions. Useful conversations. Find the people who know your stack.
Managing Diamond Dependencies in HPC with Spack's Constraint Solver
Learn how Spack's constraint-based solver handles diamond dependencies in HPC environments to prevent version conflicts and ensure reproducible software stacks.
Stop Installing Packages One at a Time: Spack Environments as Your Source of Truth
Ad hoc Spack installs drift into inconsistent dependency graphs. Environments fix that by concretizing your whole stack together and locking the result — here's how views, modules, and externals fit in.
Fortran's DO CONCURRENT: Telling the Compiler It's Safe to Go Fast
DO CONCURRENT lets you promise the compiler that loop iterations are independent, unlocking vectorization and parallel execution without OpenMP directives — if you can keep that promise.
How can I prevent MPI initialization errors when running Ansys on a Slurm cluster with InfiniBand?
Deploying Ansys workloads on a Linux HPC cluster managed by Slurm and equipped with InfiniBand occasionally results in MPI initialization errors such as "multiple pkey found" or connection failures. The objective is to run Ansys simulations reliably without these MPI‑related obstacles, while respecting the cluster’s job scheduling constraints and ensuring th
ansysHow to apply least‑privilege security when launching MAPDL via PyAnsys on a Slurm HPC cluster?
When starting MAPDL from Python using ansys.mapdl.core.launch_mapdl (as shown in the shared‑topology example), the process is launched with the user's current credentials and environment. In a Slurm‑managed cluster that also uses NFS/BeeGFS storage and MPI over InfiniBand, what specific steps should be taken to restrict the MAPDL process to the minimum requi
Which method is preferred for managing compiler overrides in shared HPC environments?
Spack utilizes compiler.json files to store paths, flags, and versioning information for detected compilers. While spack compiler find automates the creation of these entries, HPC environments often feature compilers installed in non-standard locations or specific module-loaded paths that auto-detection may overlook. In shared environments where multiple use
Which Spack variants effectively minimize the build footprint for low-traffic workloads?
When configuring a Spack environment for a low-traffic workload, reducing the overall build footprint and dependency overhead is a primary goal. The Spack concretizer allows for the specification of variants to disable unnecessary features, such as MPI or CUDA support, which can significantly decrease the number of required dependencies and total disk usage.
Spack Tcl and Lua module generation: conflicting modulefile precedence when the same package is built with different compilers
We generate both Tcl and Lua modulefiles for the same Spack installation (0.20+) using spack module tcl generate and spack module lua generate . Several packages exist in multiple variants built with different compilers, and our modules.yaml defines projections and conflict rules that we expect to apply uniformly. The uncertainty is at the integration bounda