Adaptive, Lightweight, Unified Metrics.
Alumet is a modular framework for local and distributed measurement.
Alumet provides a unified interface for gathering measurements with sources (on the right), transforming the data with models (in the middle) and writing the result to various outputs (on the left). The elements (colored rectangles) are created by plugins, on top of a standard framework.
- Extensible Framework: Alumet can easily be extended in order to make new research experiments. Leverage existing plugins and only add what you need, without reinventing the wheel. Take advantage of the unified data model and parallel measurement pipeline.
- Operational Tool: the end result is (or aims to be) a ready-to-use measurement tool that is robust, efficient and scalable.
Tools built with Alumet can be used for (non-exhaustive list):
- measuring the energy consumption of some hardware component (CPU, GPU, etc.) in an accurate and efficient way by using the latest research results1
- local system monitoring (bare-metal HPC node, cloud VM, etc.)
- distributed monitoring (datacenters, K8S clusters, etc.)
- profiling a specific application
We provide a standard Alumet agent that you can install on your system(s). For the moment, Linux is the only supported OS. Download the agent from the latest release.
Please read the Alumet User Book to learn how to install and use the Alumet "agent" (the program that performs the measurements).
If you have a question, feel free to ask on the Discussions page.
The alumet crate, aka "Alumet core", provides the measurement framework in the form of a library. This library offers a plugin system that you can use to extend Alumet in the following ways:
- read new sources of measurements
- apply arbitrary transformations to the data (such as energy attribution models)
- export the data to new outputs
- perform actions on startup and shutdown
Please read the Alumet Developer Book to learn how to make plugins and agents.
Alumet is a joint project between the LIG (computer science laboratory of Grenoble) and Eviden (Atos HPC R&D). It is also open to external volunteers like you!
Please go to the contributing guide to get started (work in progress).
Copyright 2024 Guillaume Raffin, BULL SAS, CNRS, INRIA, Grenoble INP-UGA. Licensed under the EUPL-1.2 or later.
You can find more information about the EUPL here. The EUPL is compatible with many other open source licenses and shares some principles with the well-known LGPL.
Footnotes
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See the following research paper for a detailed analysis of some common errors in RAPL-based measurement tools: Guillaume Raffin, Denis Trystram. Dissecting the software-based measurement of CPU energy consumption: a comparative analysis. 2024. ⟨hal-04420527v2⟩. ↩