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greenAlgoR package

Source: R/greenAlgo-package.R
greenAlgoR-package.Rd

Carbon Footprint Estimation for R Computations

The greenAlgoR package provides tools to estimate the carbon footprint and energy consumption of computational tasks in R. Based on the Green Algorithms framework developed by Lannelongue et al. (2021), this package helps researchers and data scientists understand and minimize the environmental impact of their work.

Internal Data

The package includes some internal datasets to work outline. You can access them directly after loading the package (TDP_cpu_internal, carbon_intensity_internal and ref_value_internal). You can also replace default data by overwritting them. By default, data are loaded using functions:

TDP_cpu_internal <- csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/CPUs.csv")

carbon_intensity_internal <- csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/CI_aggregated.csv")

ref_value_internal <- csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/referenceValues.csv")

Main Functions

  • ga_footprint: Calculate carbon footprint for individual computations

  • ga_targets: Calculate carbon footprint for targets pipelines

  • session_runtime: Compute session runtime and memory usage

Key Features

  • Estimate CO2 emissions based on runtime, CPU usage, and memory consumption

  • Support for different geographical locations with varying carbon intensities

  • Integration with the targets package for pipeline analysis

  • Visualization tools for carbon footprint comparisons

  • Configurable hardware specifications (CPU models, memory, storage)

Getting Started

To get started with greenAlgoR, try:


# Basic usage - estimate footprint of a 12-hour computation
result <- ga_footprint(runtime_h = 12, location_code = "WORLD")

# For your current R session
session_footprint <- ga_footprint(runtime_h = "session")

# For targets pipelines (in a targets project)
targets_footprint <- ga_targets()

References

Lannelongue, L., Grealey, J., Inouye, M. (2021). Green Algorithms: Quantifying the Carbon Footprint of Computation. Advanced Science, 8(12), 2100707. doi:10.1002/advs.202100707

Author

Adrien Taudière adrien.taudiere@zaclys.net