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"author": "Sorelle Friedler, Kadan Lottick, Silvia Susai",
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"description": "# energyusage\n\nA Python package that measures the environmental impact of computation. Provides a function to\nevaluate the energy usage and related carbon emissions of another function.\nEmissions are calculated based on the user's location via the GeoJS API and that location's\nenergy mix data (sources: US E.I.A and eGRID for the year 2016).\n\n## Installation\n\nTo install, simply `$ pip install energyusage`.\n\n## Usage\n\nTo evaluate the emissions of a function, just call `energyusage.evaluate` with the function\nname and the arguments it requires.\n\n```python\nimport energyusage\n\n# user function to be evaluated\ndef recursive_fib(n):\n if (n <= 2): return 1\n else: return recursive_fib(n-1) + recursive_fib(n-2)\n\nenergyusage.evaluate(recursive_fib, 40, pdf=True)\n# returns 102,334,155\n```\nIt will return the value of your function, while also printing out the energy usage report on the command line.\nOptional keyword arguments:\n* `pdf`(default = `False`): generates a PDF report, alongside the command-line utility\n* `powerLoss` (default = `0.8`): accounts for PSU loss, can be set by user if known for higher accuracy of results\n* `energyOutput` (default = `False`): prints amount of energy used by the process and time taken. The order is time, enery used, return of function\n* `printToScreen` (default = `True`): controls whether there is a terminal printout of the package running\n\n### Energy Report\nThe report that will be printed out will look like the one below. The second and third lines will show a real-time reading that disappears once the process has finished evaluating.\n```\nLocation: Pennsylvania\n--------------------------------------------------------------------------------\n------------------------------- Final Readings -------------------------------\n--------------------------------------------------------------------------------\nAverage baseline wattage: 4.99 watts\nAverage total wattage: 18.32 watts\nAverage process wattage: 13.33 watts\nProcess duration: 0:00:16\n--------------------------------------------------------------------------------\n------------------------------- Energy Data -------------------------------\n--------------------------------------------------------------------------------\n Energy mix in Pennsylvania \nCoal: 25.40%\nOil: 0.20%\nNatural Gas: 31.60%\nLow Carbon: 42.50%\n--------------------------------------------------------------------------------\n------------------------------- Emissions -------------------------------\n--------------------------------------------------------------------------------\nEffective emission: 3.00e-05 kg CO2\nEquivalent miles driven: 1.23e-11 miles\nEquivalent minutes of 32-inch LCD TV watched: 1.85e-02 minutes\nPercentage of CO2 used in a US household/day: 9.86e-12%\n--------------------------------------------------------------------------------\n------------------------- Assumed Carbon Equivalencies -------------------------\n--------------------------------------------------------------------------------\nCoal: 996 kg CO2/MWh\nPetroleum: 817 kg CO2/MWh\nNatural gas: 744 kg CO2/MWh\nLow carbon: 0 kg CO2/MWh\n--------------------------------------------------------------------------------\n------------------------- Emissions Comparison -------------------------\n--------------------------------------------------------------------------------\n Quantities below expressed in kg CO2 \n US Europe Global minus US/Europe\nMax: Wyoming 7.10e-05 Kosovo 7.28e-05 Mongolia 7.13e-05\nMedian: Mississippi 3.29e-05 Ukraine 5.09e-05 Korea, South 5.82e-05\nMin: Vermont 1.99e-06 Iceland 1.31e-05 Bhutan 8.11e-06\n--------------------------------------------------------------------------------\n--------------------------------------------------------------------------------\nProcess used: 7.72e-05 kWh\n```\nThe report is divided into several sections.\n* **Final Readings**: Presents an average of:\n\t* *Average baseline wattage*: your computer's average power usage minus the process, ran for 10 seconds before starting your process\n\t* *Average total wattage*: your computer's average power usage while the process runs\n\t* *Average process usage*: the difference between the baseline and total, highlighting the usage solely from the specific process you evaluated\n\t* *Process duration*: how long your program ran for\n\n* **Energy Data**: The energy mix of the location.\n\n* **Emissions**: The effective CO2 emissions of running the program one time and some real-world equivalents to those emissions.\n\n* **Assumed Carbon Equivalencies**: The formulas used to convert from kWh to CO2 based on the energy mix of the location (for international locations, see below for more information).\n\n* **Emissions Comparison**: What the emissions would be for the same energy used in a representative group of US states and countries.\n\nNot part of a section is the energy used by the process, which is optionally printed.\n\nThe PDF report contains the same sections, but does not include the process duration or the emissions comparison momentarily.\n\n## Methodology\n### Power Measurement\n#### CPU\nWe calculate CPU power usage via the RAPL (Running Average Power Limit) interfaces found on Intel processors. These are non-architectural model-specific registers that provide power-related information\nabout the CPU. They are used primarily for limiting power consumption, but the Energy Status\nregister (MSR_PKG_ENERGY_STATUS) allows for power measurement.\n\nThe RAPL interface differentiates between several domains, based on the number of processors. For a single processor machine:\n * Package\n * Power planes:\n * Core\n * Uncore\n * DRAM\n\nFor a machine with multiple processors:\n * Package 0\n * Package 1\n * ...\n * Package n\n * DRAM\n\nPresently, we use the Package domain (or a sum of all of the domains, for a multi-processor machine), which represents the complete processor package.\n\nAs outlined by [Vince Weaver](http://web.eece.maine.edu/~vweaver/projects/rapl/), there are multiple ways to access the RAPL interface data, namely:\n * Using perf_event interface\n * Reading the underlying MSR\n * Reading the files under `/sys/class/powercap/intel-rapl/`\n\nWe elected to use the final method because it is the only one that does not require sudo access. We read the `energy_uj.txt` files inside the package folder(s) `intel-rapl:*`. These files represent the energy used in microjoules, and they update roughly every millisecond. The value in the file increases to the point of overflow and then resets. We take 2 readings with a delay in-between, and then calculate the wattage based on the difference (energy) and the delay (time). To avoid errors due to the reset of the file, we discard negative values.\n\nFor more information on the RAPL interface, consult the [Intel\u00ae 64 and IA-32 Architectures Software Developer's Manual](https://software.intel.com/sites/default/files/managed/39/c5/325462-sdm-vol-1-2abcd-3abcd.pdf).\n\n#### GPU\nTo the package measurement we also add the power usage of the GPU for machines that have an Nvidia GPU that support the NVIDIA-smi program.\n\nThe NVIDIA-smi is a command-line utility that allows for the querying of information about the GPU. If the GPU is identified as valid, we use the built-in method to query the current wattage, and then convert the output into a float.\n\nMore information on NVIDIA-smi can be found on the [Nvidia website](https://developer.nvidia.com/nvidia-system-management-interface).\n\n\n### Calculating CO2 Emissions\n#### Location\nIn order to accurately calculate the CO\u2082 emissions associated with the computational power used, we determine the geographical location of the user via their IP address with the help of the [GeoJS](https://www.geojs.io/) API. If the location cannot be determined, we use the United States as the default.\n\nLocation is especially important as the emissions differ based on the country's (and, in the case of the United States, the state's) energy mix.\n\n#### Energy Mix Information\nWe obtained international energy mix data from the [U.S. Energy Information Administration data](https://www.eia.gov/beta/international/data/browser/#/?pa=0000000010000000000000000000000000000000000000000000000000u&c=ruvvvvvfvsujvv1vrvvvvfvvvvvvfvvvou20evvvfvrvvvvvvurs&ct=0&vs=INTL.44-2-AFG-QBTU.A&cy=2016&vo=0&v=H&start=2014&end=2016) for the year 2016. Specifically, we looked at the energy consumption of countries worldwide, broken down by energy source. For the data points labeled *(s)* (meaning that the value is too small for the number of decimal places shown), we approximated those amounts to 0. No data was available for, and thus we removed from consideration, the following: Former Czechoslovakia, Former Serbia and Montenegro, Former U.S.S.R., Former Yugoslavia, Hawaiian Trade Zone, East Germany and West Germany.\n\nOur United States energy mix and emissions data was obtained from the [U.S. Environmental Protection Agency eGRID data](https://www.epa.gov/sites/production/files/2018-02/egrid2016_summarytables.xlsx) for the year 2016. We used the *State Resource Mix* section for displaying the energy mix, and the *State Output Emission Rates* section for calculating emissions in the United States. We did not use the *otherFossil* data as the values were predominantly 0 (and in cases in which the value was nonzero, it was below 1%).\n\nAs of July 2019, the most recent eGRID data was from the year 2016. We elected to use 2016 U.S. E.I.A. data for consistency between the data sources.\n\n#### Conversion to CO2\nSince the international data only contained an energy mix, and no emission data, we reverse-engineered the formulas used in the eGRID data. This gives us additionally consistency between the separate datasets.\n* *Coal*: 2195.20 lbs CO2/MWh = 995.725971 kg CO2/MWh\n* *Petroleum*: lbs CO2/MWh = 816.6885263 kg CO2/MWh\n* *Natural gas*: 1639.89 lbs CO2/MWh = 743.8415916 kg CO2/MWh\n\n\n## Related Work\n* In their paper [*Energy and Policy Considerations for Deep Learning in NLP*](https://arxiv.org/abs/1906.02243), Strubell et. al not only analyze the computational power needed for training deep learning models in NLP, but further convert the data into carbon emissions and cost. Our tool aims to facilitate this analysis for developers in a single package. We do not consider cost, instead choosing to focus solely on the environmental impact. Further, we do not focus on a specific computational area. We also extend their analysis of carbon emissions by including international data on energy consumption and CO2 emissions for localized analysis of the carbon footprint of the tested program.\n\n## Limitations\n* Due to the methods in which the energy measurement is being done (through the Intel RAPL\ninterface and NVIDIA-smi), our package is only available on Linux kernels that have the\nRAPL interface and/or machines with an Nvidia GPU.\n\n* A country\u2019s overall energy consumption mix is not necessarily representative of the mix of energy sources used to produce electricity (and even electricity production is not necessarily representative of electricity consumption due to imports/exports). However, the E.I.A. data is the most geographically comprehensive that we have found. We are working on obtaining even more accurate data.\n\n\n## Acknowledgements\nWe would like to thank [Jon Wilson](https://www.haverford.edu/users/jwilson) for all his valuable insight with regards to the environmental aspect of our project.\n\n\n",
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