Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm
Source:R/ga_footprint.R
ga_footprint.Rd
Please cite Lannelongue, L., Grealey, J., Inouye, M., Green Algorithms: Quantifying the Carbon Footprint of Computation. Adv. Sci. 2021, 2100707. https://doi.org/10.1002/advs.202100707
Default value are from https://github.com/GreenAlgorithms/green-algorithms-tool:
PUE: https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/default_PUE.csv
TDP_per_core: https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/CPUs.csv
power_draw_per_gb: https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707
Description of the algorithm from the green-algorithms website:
"""
The carbon footprint is calculated by estimating the energy draw of the algorithm and the carbon intensity of producing this energy at a given location:
$$carbon footprint = energy needed * carbon intensity$$
Where the energy needed is:
$$runtime * (power draw for cores * usage + power draw for memory) * PUE * PSF$$
The power draw for the computing cores depends on the model and number of cores, while the memory power draw only depends on the size of memory available. The usage factor corrects for the real core usage (default is 1, i.e. full usage). The PUE (Power Usage Effectiveness) measures how much extra energy is needed to operate the data centre (cooling, lighting etc.).
The PSF (Pragmatic Scaling Factor) is used to take into account multiple identical runs (e.g. for testing or optimisation).
The Carbon Intensity depends on the location and the technologies used to produce electricity. But note that the "energy needed" [...] is independent of the location.
"""
Usage
ga_footprint(
runtime_h = NULL,
location_code = "WORLD",
PUE = 1.67,
TDP_per_core = 12,
n_cores = 1,
cpu_model = "Any",
memory_ram = NULL,
power_draw_per_gb = 0.3725,
PSF = 1,
usage_core = 1,
add_ref_values = TRUE,
add_storage_estimation = FALSE,
mass_storage = NULL,
carbon_intensity = NULL,
TDP_cpu = NULL,
ref_value = NULL
)Arguments
- runtime_h
Runtime in hours (numeric). Use a positive number for explicit runtime, or "session" to automatically calculate based on current R session time using
proc.time().- location_code
Character string specifying geographical location for carbon intensity. Available options include country codes (e.g., "FR", "US", "CN") or "WORLD" for global average. See the Green Algorithms database for complete list of supported locations.
- PUE
Power Usage Effectiveness (numeric, default 1.67). Measures data center efficiency - how much extra energy is needed for cooling, lighting, etc. Use 1.05 for personal computers, 1.2-1.7 for data centers. See https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/default_PUE.csv
- TDP_per_core
Thermal Design Power per core in Watts (numeric, default 12). CPU power consumption per core. Find values at https://www.techpowerup.com/cpu-specs/ or http://calculator.green-algorithms.org/. Overridden by
cpu_modelparameter.- n_cores
Number of CPU cores (integer, default 1). Overridden by
cpu_modelparameter.- cpu_model
Character string specifying exact CPU model. Must match entries in the Green Algorithms database. When specified, automatically sets
TDP_per_coreandn_cores. Use "Any" for generic calculation.- memory_ram
RAM memory in GB (numeric). If NULL, attempts to detect automatically using
benchmarkme::get_ram().- power_draw_per_gb
Power consumption per GB of RAM in Watts (numeric, default 0.3725).
- PSF
Pragmatic Scaling Factor (numeric, default 1). Accounts for multiple runs of the same computation. As noted by Lannelongue et al. (2021): "computations are rarely performed only once" - use values > 1 to account for repeated runs, parameter sweeps, or iterative development. GHG emissions are multiplied."
- usage_core
(int, default 1). The usage factor corrects for the real core usage (default is 1, i.e. full usage).
- add_ref_values
(logical, default TRUE) Do we compute and return reference values to compare to your footprint ?
- add_storage_estimation
(logical, default FALSE) Do we compute the footprint of mass storage ? By default FALSE because it is far less important than cpu and memory usage. Note that green-algorithms original tool do not compute mass storage usage.
- mass_storage
(int. in GB, default NULL) The size of the mass_storage. Only used if add_storage_estimation is set to TRUE. If set to NULL, use the
base::gc()function to estimate storage used.- carbon_intensity
(default NULL). Advanced users only. A dataframe with
locationandcarbonIntensitycolumns. Set to carbon_intensity_internal if NULL. carbon_intensity_internal is set using command line csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/CI_aggregated.csv")- TDP_cpu
(default NULL). Advanced users only. A dataframe with
model,n_coresandTDP_per_corecolumns. Set to TDP_cpu_internal if NULL. TDP_cpu_internal is set using command line csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/CPUs.csv")- ref_value
(default NULL). Advanced users only. A dataframe with
variableandvaluecolumns. Set to ref_value_internal if NULL. ref_value_internal is set using command line csv_from_url_ga("https://raw.githubusercontent.com/GreenAlgorithms/GA-data/5266caba6601dae0ffc93af8971e758f55292e08/v3.0/referenceValues.csv")
Value
A list of values
runtime_h: the input run time in hourslocation_code: the input location codeTDP_per_core: the input TDP_per_core (if cpu_model is set, correspond to the TDP_per_core for this cpu)n_cores: the input n_cores (if cpu_model is set, correspond to the n_cores for this cpu)cpu_model: the input cpu model. If set to "Any", TDP_per_core and ncore are usedmemory_ram: the input memory ram in GBpower_draw_per_gb: the input power draw per GBusage_core: the input usage corecarbon_intensity: the input carbon intensity (depend on location code)PUE: the input PUEPSF: the input PUEpower_draw_for_cores_kWh: the output power draw for cores in kWhpower_draw_for_memory_kWh: the output power draw for RAM memory in kWhenergy_needed_kWh: the output energy needed in kWhcarbon_footprint_cores: the output carbon footprint in grams of CO2 for cores usagecarbon_footprint_memory: the output carbon footprint in grams of CO2 for memory usagecarbon_footprint_total_gCO2: the total output carbon footprint in grams of CO2ref_value: (optionnal, return if add_ref_values is TRUE) : a dataframepower_draw_storage_kWh: (optionnal, return if add_storage_estimation is TRUE) the output power draw for mass storage in kWh
Examples
# Basic usage with explicit parameters
result <- ga_footprint(
runtime_h = 2,
n_cores = 4,
TDP_per_core = 15,
memory_ram = 16,
location_code = "WORLD"
)
result$carbon_footprint_total_gCO2
#> [1] 104.6455
# Using specific CPU model (automatically sets cores and TDP)
ga_footprint(
runtime_h = 1,
cpu_model = "Core i5-9600KF",
location_code = "FR"
)
#> $runtime_h
#> [1] 1
#>
#> $location_code
#> [1] "FR"
#>
#> $TDP_per_core
#> [1] 15.8
#>
#> $n_cores
#> [1] 6
#>
#> $cpu_model
#> [1] "Core i5-9600KF"
#>
#> $memory_ram
#> [1] 16.77258
#>
#> $power_draw_per_gb
#> [1] 0.3725
#>
#> $`usage core`
#> [1] 1
#>
#> $carbon_intensity
#> [1] 51.28
#>
#> $PUE
#> [1] 1.67
#>
#> $PSF
#> [1] 1
#>
#> $power_draw_for_cores_kWh
#> [1] 0.158316
#>
#> $power_draw_for_memory_kWh
#> [1] 0.0104338
#>
#> $energy_needed_kWh
#> [1] 0.1687498
#>
#> $carbon_footprint_cores
#> [1] 8.118444
#>
#> $carbon_footprint_memory
#> [1] 0.5350454
#>
#> $carbon_footprint_total_gCO2
#> [1] 8.65349
#>
#> $ref_value
#> variable value
#> 1 Total 8.65348986119868
#> 2 Cores 0.158316
#> 3 Memory 0.0104338022854656
#> 4 memoryPower 0.3725
#> 5 google_search 10
#> 6 streaming_netflix_perhour 36
#> 7 train_perkm 41
#> 8 flight_economy_perkm 171
#> 9 passengerCar_EU_perkm 175
#> 10 passengerCar_US_perkm 251
#> 11 tree_month 917
#> 12 treeYear 11000
#> 13 flight_PAR-LON 50000
#> 14 flight_PAR-DUB 110000
#> 15 flight_NY-SF 570000
#> 16 flight_NYC-MEL 2310000
#> source
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html
#> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html
#> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update)
#> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020
#> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020
#> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019
#> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle
#> 11 gCO2 same source as treeYear
#> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640
#> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 16 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> prop_footprint
#> 1 1.000000e+00
#> 2 1.829505e-02
#> 3 1.205733e-03
#> 4 4.304622e-02
#> 5 1.155603e+00
#> 6 4.160171e+00
#> 7 4.737973e+00
#> 8 1.976081e+01
#> 9 2.022305e+01
#> 10 2.900564e+01
#> 11 1.059688e+02
#> 12 1.271163e+03
#> 13 5.778016e+03
#> 14 1.271163e+04
#> 15 6.586938e+04
#> 16 2.669443e+05
#>
# Calculate footprint for current R session
ga_footprint(runtime_h = "session")
#> $runtime_h
#> user.self
#> 0.001950833
#>
#> $location_code
#> [1] "WORLD"
#>
#> $TDP_per_core
#> [1] 12
#>
#> $n_cores
#> [1] 1
#>
#> $cpu_model
#> [1] "Any"
#>
#> $memory_ram
#> [1] 16.77258
#>
#> $power_draw_per_gb
#> [1] 0.3725
#>
#> $`usage core`
#> [1] 1
#>
#> $carbon_intensity
#> [1] 475
#>
#> $PUE
#> [1] 1.67
#>
#> $PSF
#> [1] 1
#>
#> $power_draw_for_cores_kWh
#> user.self
#> 3.90947e-05
#>
#> $power_draw_for_memory_kWh
#> user.self
#> 2.035461e-05
#>
#> $energy_needed_kWh
#> user.self
#> 5.944931e-05
#>
#> $carbon_footprint_cores
#> user.self
#> 0.01856998
#>
#> $carbon_footprint_memory
#> user.self
#> 0.009668439
#>
#> $carbon_footprint_total_gCO2
#> user.self
#> 0.02823842
#>
#> $ref_value
#> variable value
#> 1 Total 0.0282384219136505
#> 2 Cores 3.90947e-05
#> 3 Memory 2.03546092918958e-05
#> 4 memoryPower 0.3725
#> 5 google_search 10
#> 6 streaming_netflix_perhour 36
#> 7 train_perkm 41
#> 8 flight_economy_perkm 171
#> 9 passengerCar_EU_perkm 175
#> 10 passengerCar_US_perkm 251
#> 11 tree_month 917
#> 12 treeYear 11000
#> 13 flight_PAR-LON 50000
#> 14 flight_PAR-DUB 110000
#> 15 flight_NY-SF 570000
#> 16 flight_NYC-MEL 2310000
#> source
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html
#> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html
#> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update)
#> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020
#> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020
#> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019
#> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle
#> 11 gCO2 same source as treeYear
#> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640
#> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> 16 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3
#> prop_footprint
#> 1 1.000000e+00
#> 2 1.384451e-03
#> 3 7.208126e-04
#> 4 1.319125e+01
#> 5 3.541274e+02
#> 6 1.274859e+03
#> 7 1.451922e+03
#> 8 6.055579e+03
#> 9 6.197230e+03
#> 10 8.888599e+03
#> 11 3.247349e+04
#> 12 3.895402e+05
#> 13 1.770637e+06
#> 14 3.895402e+06
#> 15 2.018526e+07
#> 16 8.180344e+07
#>
# Compare different locations
locations <- c("WORLD", "FR", "US", "NO")
sapply(locations, function(loc) {
ga_footprint(runtime_h = 1, location_code = loc)$carbon_footprint_total_gCO2
})
#> WORLD FR US NO
#> 14.4750561 1.5626966 12.9190637 0.2322104
# Advanced usage with storage estimation and reference values
res_ga <- ga_footprint(
runtime_h = 4,
n_cores = 8,
memory_ram = 32,
add_storage_estimation = TRUE,
add_ref_values = TRUE
)
ggplot(res_ga$ref_value, aes(y = variable, x = as.numeric(value), fill = log10(prop_footprint))) +
geom_col() +
geom_col(data = data.frame(
variable = "Total",
value = res_ga$carbon_footprint_total_gCO2
), fill = "grey30") +
geom_col(data = data.frame(
variable = "Cores",
value = res_ga$carbon_footprint_cores
), fill = "darkred") +
geom_col(data = data.frame(
variable = "Memory",
value = res_ga$carbon_footprint_memory
), fill = "orange") +
geom_col(data = data.frame(
variable = "Mass storage",
value = res_ga$carbon_footprint_storage
), fill = "violet") +
scale_x_continuous(
trans = "log1p",
breaks = c(0, 10^c(1:max(log1p(as.numeric(res_ga$ref_value$value)))))
) +
geom_vline(
xintercept = res_ga$carbon_footprint_total_gCO2,
col = "grey30", lwd = 1.2
) +
geom_label(aes(label = round_conditionaly(prop_footprint)),
fill = "grey90", position = position_stack(vjust = 1.1)
) +
labs(
title = "Carbon footprint of the analysis",
subtitle = paste0(
"(", res_ga$carbon_footprint_total_gCO2,
" g CO2", ")"
),
caption = "Please cite Lannelongue et al. 2021 (10.1002/advs.202100707)"
) +
xlab("Carbon footprint (g CO2) in log10") +
ylab("Modality") +
theme(legend.position = "none")
