Using GPU servers for climate modeling embedded with vast geological, geographical and anthropomorphic information, and interspersed with millions of variables, is right in the ballpark of Graphical Processing Units (GPUs). Why GPUs though? Let’s find out.
What is Climate Modelling?
Climate models are computer programs coded to run quantitative cause-and-effect simulations in response to any modifications in the surrounding environment. These modifications may be of various origins including (but not limited to) –
| Natural Atmospheric changes Tectonic activity Volcanic eruptions, etc | Anthropogenic Overpopulation Environmental Pollution Emission of Greenhouse gases Agriculture-induced deforestation Extinction of Species | Cosmological Solar radiation Asteroid impact Changes in Earth’s magnetic orientation |
These variables are mathematically introduced in climate model calculations in algorithmic form, and changes in variables or the underlying algorithms can be visually depicted through simulations covering the entirety of the earth, or areas as small as a town.
Not all climate models offer quantifiable data though, some are designed to be qualitative or descriptive, generating if-else scenarios in response to the input variables, or predicting future climate patterns using historic geological data.
Incorporation of different variables in Climate models: A timeline (Source)
What is the Importance of Climate Modelling?
Climate change is wreaking havoc of unprecedented proportions. It has introduced a multitude of near-irreversible effects that are not only overturning the geophysical characteristics of the earth, but are also distressing the entire value chain of life.
Be it the melting of glaciers, rising sea levels, changing patterns of rainfall, extreme heatwaves, or disturbed soil moisture and mineral levels due to human interference, the interconnectedness is gargantuan. A minute alteration in the earth’s system has the potential to alter our lives in myriad adverse ways.
A change of such magnitude, its manifolds repercussions and mitigation strategies, can only be comprehended and explained via detail-oriented climate modelling.
What are the Benefits of GPU servers for climate modeling?
1. Better understanding of our environment:
Climate models are used to predict the upheavals the earth’s climatic conditions has undergone/ will undergo over a given period. These models can be programmed to factor real-time variables as well as worst-case scenarios such as keystone species extinction or unmitigated natural disasters.
2. Assisting biological research:
Climate models enable scientists to not only accurately predict changes in temperature-precipitation, but also the evolution/ regression of species in response to these changes. This has applications in various biological sciences such as palaeontology.
3. Comprehending climate patterns and anomalies:
Climate models are crystal balls for meteorologists enabling them to compare weather conditions, predict natural disasters and future climate changes. This information can be used to ascertain if these intermittent disturbances are normal or are, in fact, distress signals emanating from global warming or shifting climatic conditions.
4. Quantifying anthropogenic influence:
Climate models also help scientists understand what kind of human interferences or influences are at play in manifesting said climate changes. Note that these are purely scientific calculations based on evidence, the Ghost of Christmas Yet to Come, empowering us to make informed decisions to allay the worst impact and remediate the damage already done.
5. Testing mitigation strategies:
As governments and institutions across the world pour in trillions of dollars into achieving “Net Zero” targets, it is imperative to certify that the mitigation strategies being funded and implemented align with the climate goal achievements. Failing this, mid-term course correction must also be viable.
Given its multifarious benefits, the construction of virtual climate models that can simulate the Earth’s various processes over a given time-period can be termed as one of the most prominent scientific achievements in the last few decades.
What are the Computational Challenges of Climate Modelling?
There is no second earth.
We have been hearing this for too long. The technology warriors are now up in arms ably supporting climate activists, rationalists, and environmentalists worldwide to chart out the dangers lurking and predicting the perilous future as well as ways to mitigate this quandary!
Developing a climate model is no easy task. You must understand this by now. It takes zillions of data points simulating the changes in the atmospheric, lithospheric and oceanic environments, besides biological and cosmological influences. These data points are, in turn, used to generate mathematical equations that form a kind of lattice representing the entire globe, rendering analytical constructs.
Decoding this deluge of constructs is an intensive process that involves determining a hypothesis, testing that hypothesis and drawing inferences. The inferences are the framework within which more hypotheses are tested for the future climate system. The finer the lattice structure is, more accurate the prediction is.
20 parameters that must be incorporated in climate models (Source)
However, ponder a little about the extensive calculations required in developing a climate model. Additionally, the governing algorithms must be repeatedly recalibrated to include/ exclude some variables or modify their respective weights. Most climate models today have well over one million lines of code! Moreover, each doubling of such model’s precision requires 10x enhancement in computing power over the previous iteration. Increasing the model’s horizontal resolution from 50 km to 2 km requires 253 times, i.e., over 15,000 times, enhancement in the computational effort.
Deploying more advanced computing systems to handle these calculations enable more detailed and clear rendering of the lattice or the digital twin. Being able to visualize changes in the lattice in real-time in response to changes in input variables allows researchers to study multiple simulations with diverse levels of complexity.
Another key bottleneck for climate modelling is memory bandwidth – the transfer of humongous volumes of data from the memory to the processors and vice-versa.
Accuracy, unfortunately, has been yet another challenge in developing high-definition climate models. The input data points are usually a mix of extrapolations of historical data and currently observable atmospheric features. Several data points aren’t captured at all, like the dynamic ability of clouds to reflect incoming sunlight, or the effects on various ecosystems of the cooling of earth’s core, or the havoc being wreaked on oceanic carbon sequestration cycles by human-induced loss of lifeforms like phytoplankton. These ‘missed-out’ data points can massively impact the predictions and even render the entire process potentially faulty or way off the mark.
What is the role of GPUs in Climate Modelling?
Every attempt at climate modelling and simulation necessitates some common steps as outlined here:
- Developing an elaborate and precise construct with the given variables to accurately render a “digital twin” of the earth/specific regions
- Incorporating modifiable estimates regarding non-quantifiable phenomenon such as airflow patterns around physical features, oceanic currents at great depths, etc
- Testing multi-variable hypotheses within the parameters defined by the above-mentioned construct and generating real-time predictions
- Construct mathematically accurate, irrefutable models, given that whereas short-period weather predictions can be testable readily, climate models project long term changes which cannot be verified within human lifetime.
Each of the above-mentioned stages elicit GPU utilization for computation acceleration. GPU’s inherent architecture further contributes to its inevitable use in climate modelling clusters:
- The hundreds of parallel processing cores in GPU deliver explosive increase in calculation frequency, making it ideal for developing the most granular climate model. Nvidia’s A100 GPU can outperform the most advanced CPU by 237 times in AI/ ML inference benchmarks. AI/ ML form the bedrock on which most advanced climate models are constructed.
- Furthermore, GPU is optimized for performing floating-point calculations making it possible to deliver high computational accuracy without compromising on the resolution of the eventual model.
- GPU has access to the more efficient VRAM (Video RAM) in volume sufficient to be loaded with several gigabytes worth of raw data, variables and algorithms. This also enables GPU to generate lag-free visual simulations of the model.
- GPU memory bandwidth is substantially higher than that of the most advanced CPUs. Climate models require continuous access to colossal quantum of data and consequentially system lags and slowing down are perpetual possibilities. GPU’s flexible, programmable architecture and high memory bandwidth act as enablers to climate modelling and associated AI/ ML and there is no latency even when there is a data context switch spurring the use of ML operations for developing artificial neural networks or inference applications.
- ML-assisted climate prediction and inference systems are computationally intensive to run, but even more difficult to train. They require highly efficient, high-performance computing systems (HPC) capable of crunching humungous volumes of data, subjecting these data points simultaneously to complex calculations, and extracting precise inferences. GPU form the core component of most HPC clusters given that they constitute the one-stop solution to these several concerns.
- Advanced GPU such as Nvidia’s enterprise-class A100 and A30 can be segmented into multiple virtual instances, with each instance capable of handling a unique workload/ computation as required by the model’s flow. It is easier to develop and deploy the climate model by fragmenting into smaller, independent applications with minimal inter-dependences.
- It is more efficient in terms of turnover time and manpower costs to perform complex calculations on a GPU rather than a CPU array of equivalent capability. Development time and manpower costs constitute the most critical expenses when constructing a climate model given that it must undergo multiple iterations and code refactoring.
- Nvidia also plans on extending confidential computing technology to the realm of GPU with its upcoming Hopper architecture. Confidential computing isolates the data being processed in an encrypted area which is accessible only to authorized entities and invisible to everyone else. The possibility of processing data in a secure environment, irrespective of the fact that it may be hosted on Public Cloud GPUs, can act as a force multiplier for enterprises implementing secure coding practices. This will effectively nourish the AI/ ML ecosystem across industries without incurring additional expenditure. Confidential computing on CPU is available currently from Intel, Arm and AMD, but Nvidia H100 will make it available for the first time on GPU.
Confidential computing has applications across industries where sensitive and/or regulated data is used for AI/ ML training and inference (Source: Nvidia)
What is the Future of Climate Modelling using GPU?
Climate change affects every lifeform on our planet. Climate science today is at a pivotal junction where incremental advancements in technology are contributing to quantum leaps in the evolution of climate models.
GPU is the enabler for harnessing the available computing power and fundamental knowledge repositories for the benefit of humanity. Leading GPU manufacturers are also collaborating/ investing in projects developing advanced climate models or bettering the existing ones.
Nvidia is working on Earth-2, a supercomputer that will create a physically accurate, ultra high-resolution “digital twin” of the entire earth at the ground-breaking resolution of one metre! Earth-2 will be constructed using Modulus and Omniverse. Whereas Omniverse is Nvidia’s in-house multi-GPU development platform for 3D simulations, Modulus is a framework for developing AI-ML models and training them on the principles of physics using terabytes of data. Supplemented by GPU-accelerated computing, artificial intelligence, neural networks and deep learning, Earth-2 will be the replica on which experiments will be run to accurately and quickly predict climate phenomenon and identify mitigation strategies.
GPUs of the future will not merely be restricted to churning humongous data volumes or creating another digital playground in the offing. They are intended for impacting the real world substantially, one challenge at a time, one digital twin at a time. And improving climate modelling simulations is just one aspect of their actual capabilities. We sure live in exciting times!
Related Post
Get in Touch
Credits First!
- 24*7 Human Support
- Pay-as-you-go Pricing
- No Egress Cost
- Multi Tier Security