Abstract

Spack is a flexible multi-language package manager for HPC, Data Science, and AI, designed to support multiple versions, configurations, and compilers of software on the same system. Since the last FOSDEM, the Spack community has reached a major milestone with the release of Spack v1.0, followed closely by v1.1. This talk will provide a comprehensive overview of the "What's New" in these releases, highlighting the changes that improve robustness, performance, and user experience. We will cover among other things the shift to modeling compilers as dependencies, the package repository split, and the new jobserver-aware parallel installer.

Description

With the release of Spack v1.0 in July 2025 and v1.1 in November 2025, the project has introduced significant architectural changes and new features requested by the community. In this talk, we will dive into the key features introduced across these releases:

• Compilers as dependencies. Spack has fulfilled an old promise from FOSDEM 2018. Compilers are modeled as first-class dependencies, dependency resolution is more accurate, and binary distribution and ABI compatibility checks are more robust.
• The separation of the package repository from the core tool and the introduction of a versioned Package API allows users to pin the package repository version independently from Spack itself and enables regular package repository releases.
• Parallel builds with a new user interface. Spack has a new scheduler that coordinates parallel builds using the POSIX jobserver protocol, allowing efficient resource sharing across all build processes. The decades-old jobserver protocol is experiencing a major renaissance, adopted recently by Ninja v1.13 (July 2025) and the upcoming LLVM 22 release. We’ll talk about how this enables composable parallelism across make, ninja, cargo, GCC, LLVM, Spack, and other tools.

Expected Prior Knowledge / Intended Audience

This talk is aimed at Research Software Engineers (RSEs), HPC system administrators, and Data Scientists who use or manage software stacks. Familiarity with Spack is helpful but not strictly required; the talk will be accessible to anyone interested in package management and software reproducibility in scientific computing.

Links

• Spack Website: https://spack.io
• Spack GitHub: https://github.com/spack/spack
• Spack Packages: https://github.com/spack/spack-packages