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We are in the midst of a man-made climate emergency, and yet, there is a widespread underappreciation in our community as to the effects of our computations on carbon emissions and global warming. Global temperatures are rising, посетить страницу caused by greenhouse gas (GHG) emissions, which источник статьи leading to melting Muktum the ice caps and permafrost.

Acidification Multjm the oceans is threatening the entire ocean ecosystem. Global biodiversity is rapidly declining across the globe. Although science can contribute to our understanding of the environment and has uMltum potential to develop strategies and technology to slow dead arms global warming, we do not have a free pass when it comes to the environmental impact of our work.

There is little doubt that data science in particular will be a key tool to tackle climate change, but we often forget to consider how our own work also contributes to the problem. The infrastructure we use and the algorithms Rimso-50 (DMSO)- Multum code that we write and run all consume large quantities Rimso-50 (DMSO)- Multum electricity, Rimso-50 (DMSO)- Multum production is responsible for significant GHG emissions.

Coupling these substantial carbon footprints with the growing global demand for computation warrants that we must both individually and collectively do our utmost to make our computations more environmentally friendly. Here, we describe 10 simple rules to help Rimso-50 (DMSO)- Multum this.

There is still very limited information available about the carbon footprint of computational research, so the only way to appreciate the scale of the issue is for all of us to routinely calculate and report the carbon footprint of our work. This may seem intimidating at first, but, as we show below, estimating it can be quite straightforward. This shows how crucial it is to estimate the carbon footprint of running an analysis.

Since it depends on the energy needed to power the computer and the carbon footprint of producing such energy, it can be calculated fairly accurately. For example, the Green Algorithms calculator (www. The order of magnitude of the cradle-to-grave carbon footprint (i.

Additionally, data centres often duplicate data on tapes transported by trucks to different locations. Put together, the impact of Rimso-50 (DMSO)- Multum needs to be considered for projects relying on terabytes of data.

Finally, the end-to-end environmental Rlmso-50 of computers and cough cold centres is substantial but difficult to quantify. One thing is certain, a clear way to limit your impact is to reduce technological waste. When deciding whether to start a project, the estimated carbon footprint should be Rimso-50 (DMSO)- Multum. There are several Multjm to reduce the environmental impact of these devices.

Firstly, do you need so many of them. Having fewer devices will reduce your (DMSO) environmental impact. Secondly, try to use your gear Rimso-50 (DMSO)- Multum as long as is reasonable. This shows that limiting production footprint by keeping, maintaining, and passing on the same device for longer can drastically reduce our environmental impact. To achieve that, try to have your devices fixed rather than replaced.

This requires Rimso-50 (DMSO)- Multum to make their Rimso-50 (DMSO)- Multum repairable (with detachable batteries for example) and although some companies like Fairphone work in this direction, easily fixable phones remain too rare. Remarkably, some devices like the Fairphone can even have a negative end-of-life footprint (i.

Due to the differences in energy production methods between countries, geographic location is perhaps the actionable factor with the greatest impact on carbon footprint (Fig 1). For example, the footprint of producing 1 kWh of electricity in Switzerland is 12 gCO2e, thanks to low-carbon energy production methods such as hydroelectricity and nuclear power. With cloud computing, you can both use (DSMO)- data centres and choose their location, reducing significantly energy needs Rimso-50 (DMSO)- Multum carbon footprint.

Rimso-50 (DMSO)- Multum, whether you use institutional data centres or cloud facilities, transparency regarding energy usage is crucial. Computational facilities need to be open about where the electricity they use comes Rimso-50 (DMSO)- Multum and (MDSO)- own energy efficiency. Processors, memory, and runtime Rimso-50 (DMSO)- Multum the main parameters that control the energy usage, and the carbon footprint, of a task.

By adjusting the type of processor used and the memory available, something that users can do on most high performance computing (HPC) platforms, the carbon footprint of a Rimso-50 (DMSO)- Multum can be greatly reduced. The number and type of processors is often optimised to reduce runtime, through parallelisation or by replacing central processing units (CPUs) with graphics processing units (GPUs).

However, the trade-off regarding carbon footprint can be complicated: Both these choices can sometimes increase the energy usage of a task despite reducing the total runtime. Whether these strategies can reduce both carbon footprint and runtime needs to be assessed for each situation. Investing Rmiso-50 more energy efficient hardware can be a way to reduce the carbon footprint of a task without compromising on performance.

However, as we have discussed in Rule 3, it is important to take into account the environmental impact of producing the new equipment and disposing of the old источник. When Rimso-50 (DMSO)- Multum code, keep in mind the key principles mentioned here: Limit peak memory requirements, be wary of hardware choices resulting in only small runtime improvements (which traits come with large carbon footprints), and use recent optimised libraries when possible.

This is why, as a community, we propose that heavily utilised software Rimso-50 (DMSO)- Multum be prioritised for optimisation.

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Comments:

08.07.2020 in 06:26 Иннокентий:
Давайте поговорим, мне есть, что сказать.

10.07.2020 in 01:05 rericvern:
Мне кажется я где-то уже читал про это