# The Environmental Cost of Bioinformatics at GenOuest

Rémy Siminel, Stéphanie Robin, Erell Le Deun, Matéo Boudet, Anthony BretTaudeau

► To cite this version:

Rémy Siminel, Stéphanie Robin, Erell Le Deun, Matéo Boudet, Anthony BretTaudeau. The Environmental Cost of Bioinformatics at GenOuest. JOBIM 2025 - Journées Ouvertes en Biologie, Informaticsque et Mathématiques, Jul 2025, Bordeaux, France. pp.1-1, 2025. http://05174309

HAL Id: hal-05174309

https://hal.science/hal-05174309v1

Submitted on 21 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# The Environmental Cost of Bioinformatics at GenOuest

Rémy SIMINEL¹, Stéphanie ROBIN¹,², Erell LE DEUN¹, Matéo BOUDET¹,², Anthony BRETAUDEAU¹

1 GenOuest, Univ Rennes, Inria, CNRS, IRISA, 35000, Rennes, France 2 IGEPP, INRAE, Institut Agro, France

## Context

Bioinformatics heavily depends on high-performance computing (HPC), which consumes a substantial amount of energy. As datasets expand and analytical tools grow exponentially more complex - especially with the rise of AI-driven methods - **the carbon footprint of computational research increases**, making sustainable solutions essential.

A movement to quantify the environmental costs of research has started in France, headed for now by the EcoInfo[1] or Labo 1.5[2] initiatives.

<figure><img src="image_2.png" /></figure>

Source: On Global Electricity Usage of Communication Technology: Trends to 2030, A. Andrae, T. Edler, Challenges, 2021

## GenOuest

GenOuest https://www.genouest.org/ is a bioinformatics platform based in Rennes, providing a complete bioinformatics environment to its ~850 users.

At the core of its services is a **command-line accessible HPC** cluster with over 3 000 cores and 3 PB of storage, designed to handle large-scale bioinformatics workflows efficiently.

GenOuest also provides cloud virtualization services, collaborative workspaces, pre-installed bioinformatics tools and databases for its users.

<figure><img src="image_3.png" /></figure>

## The GenOuest cluster's 2024 Estimated Carbon Footprint

<table><thead><tr><th>kgCO2eq per year</th><th>CPU<br>55 servers</th><th>GPU<br>2 servers</th><th>Storage<br>25 servers</th><th>Other*<br>40 servers</th><th>Total<br>122 servers</th></tr></thead><tbody><tr><th scope="row">Server Life Cycle</th><td>7,000</td><td>500</td><td>3,400</td><td>5,100</td><td>16,000</td></tr><tr><th scope="row">Electricity Usage</th><td>5,400</td><td>500</td><td>2,300</td><td>4,900</td><td>13,100</td></tr><tr><th scope="row">Air Conditioning</th><td>4,000</td><td>400</td><td>1,700</td><td>3,700</td><td>9,800</td></tr><tr><th scope="row">Total</th><td>16,400</td><td>1,400</td><td>7,400</td><td>13,700</td><td>38,900</td></tr></tbody></table>

### Methodology

* **Server Life Cycle** includes manufacturing, transport and end of life, translating to roughly 1.3 tCO2eq on average per server, annualized over an expected lifetime of 10 years.

* **Electricity Usage** is measured for each server in kWh by PDUs (Power Distribution Units), the carbon footprint is computed assuming that one average French kWh releases 60 gCO2eq[3].

* **Air Conditioning** is split between GenOuest and another research platform hosted in the same room. The split is proportional to the fraction of the room's total electricity usage.

### Conclusions

* **A server's manufacturing represents about 40% of its annual carbon footprint.** It is thus crucial to buy new servers only when needed to increase server lifespans.

* **GenOuest's carbon footprint is highly influenced by the substantial PUE (Power Usage Effectiveness = Total facility energy / IT equipment energy) of 1.7 of its server room.**

## Platform Usage Metrics

### User CPU Efficiency

Over 450 users in 2024

<figure><img src="image_4.png" /></figure>

### Methodology

* For each job sent to the cluster, the SLURM Workload Manager tracks its duration, number of requested CPUs and total seconds of real CPU use.

* A job's reserved CPU time is its duration * the number of requested CPUs.

* **A user's compute efficiency** is the ratio of their total hours of real CPU use over their total hours of reserved CPU time. A user's efficiency depends highly on the kind of jobs they run most often.

### Results

* Only 2.1 million CPU-hours were used out of 12 million reserved. While 100% efficiency is unrealistic (e.g. iowait), **18% overall efficiency of cluster usage in 2024 is very low**.

* **A small number of users dominate reserved CPU and GPU time**, indicating a highly skewed distribution of demand.

* **Job over-dimensioning is widespread**, with many users requesting far more resources than they need, causing an artificial workload inflation and thus a lot of idle system capacity.

* **This inefficient usage leads to phantom demand**, which justifies unnecessary hardware purchases that contribute to environmental and financial waste.

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

## Best practices for users

### To increase user's compute efficiency :

* Use tools with proper benchmarking (tools must be tested on a test dataset)

* Use a workflow manager that pre-configures resource usage

* Reserve only the necessary resources

### To reduce user's used CPU-hours :

* Monitor the effectiveness of tools
  E.g. : Falco > FastQC; DIAMOND > BLAST; OrthoFinder > OrthoMCL

* Reduce redundant computations (good traceability of calculations and data means you don't have to restart jobs)

### To reduce user's data storage :

* Delete unnecessary data (annotating data with complete metadata enables it to be properly classified)

* Compress files whenever possible (especially FASTQ files)

* Implement an archiving strategy with clear expiry timelines

* Anticipate data ownership transmissions when project members leave

### References:

[1] EcoInfo (https://ecoinfo.cnrs.fr/)

[2] Labo 1.5 (https://labos1point5.org/)

[3] Bilan Electrique 2023, F. Ciais et al., RTE, 2024

[4] Green Algorithms (https://www.green-algorithms.org/)

## GenOuest actions

### Current actions

* **CARBOGO project: Detailed reporting** of user's resource usage on the platform

* **ECOGO project: Shut down** compute nodes during off-peak hours

* **Training sessions** on best practices for efficient resource usage.

* **Increase server lifetimes**

* **Increase air conditioning temperature** setting

### Future actions

* **Carbon footprint report for each job** modeled on the Green Algorithms Project[4] and similar to usegalaxy.fr's job reports.

* **Long-term relocation to EskemmData:** a **datacenter** with a better PUE

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<footnote>*Other services = network, cloud, hosted infrastructure...</footnote>