EESSI: One Scientific Software Stack to Rule Them All
Bibliographic record
Abstract
The European Environment for Scientific Software Installations (EESSI, pronounced as “easy”) is a collaboration between different HPC sites and industry partners, with the common goal to set up a shared repository of scientific software installations that can be used on a variety of systems, regardless of which flavor/version of Linux distribution or processor architecture is used, or whether it is a full-size HPC cluster, a cloud environment or a personal workstation. The concept of the EESSI project was inspired by the Compute Canada software stack, and consists of three main layers: - a filesystem layer leveraging the established CernVM-FS technology, to globally distribute the EESSI software stack; - a compatibility layer using Gentoo Prefix, to ensure compatibility with different client operating systems (different Linux distributions, macOS, Windows Subsystem for Linux); - a software layer, hosting optimized installations of scientific software along with required dependencies, which were built for different processor architectures, and where archspec, EasyBuild and Lmod are leveraged. We use Ansible for automating the deployment of the EESSI software stack. Terraform is used for creating cloud instances which are used for development, building software, and testing. We also employ ReFrame for testing the different layers of the EESSI project, and the provided installations of scientific software applications. Finally, we use Singularity containers for having clean software build environments and for providing easy access to our software stack, for instance on machines without a native CernVM-FS client. In this talk, we will present how the EESSI project grew out of a need for more collaboration to tackle the challenges in the changing landscape of scientific software and HPC system architectures. The project structure will be explained in more detail, covering the motivation for the layered approach and the choice of tools, as well as the lessons learned from the work done by Compute Canada. The goals we have in mind and how we plan to achieve them going forward will be outlined. Finally, we will demonstrate the current pilot version of the project, and give you a feeling of the potential impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.151 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".