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While a bespoke implementation on a specific HPC architecture can return substantial speedups 65 , achieving performance without sacrificing portability is seen as crucial to avoid a solution where one has to continuously rewrite complex software for a particular hardware option.
Today, most models and data assimilation systems are still based on millions of lines of Fortran code. In addition, they adopt a fairly rigid block-structure in the context of a hybrid parallelization scheme using the Message Passing Interface MPI and Open Multiprocessing OpenMP combined with a domain knowledge-inspired or convenience-driven data flow within the model application The basis for entirely revising this approach are again generic data structures and domain-specific software concepts that separate scientific code from hardware-dependent software layers—distinguishing between the algorithmic flexibility concern of the front-end and the hardware flexibility concern of the back-end This has increased the popularity of code-generation tools and a fundamental rethinking of the structure and separation of concerns in future model developments, promising a route to radically rewrite the present monolithic and domain-specific codes.
Despite the recent flurry of machine learning projects, it is still difficult to predict how the application of machine learning will shape future developments of weather and climate models. There are approaches to build prediction models based on machine learning that beat existing predictions systems, in particular for very short for example, now-casting 75 and very long for example, multi-seasonal 76 forecasts, but also for medium-range prediction However, the majority of the weather and climate community remains skeptical regarding the use of black-box deep-learning tools for predictions and aims for hybrid modeling approaches that couple physical process models with the versatility of data-driven machine learning tools to achieve the best results In any case, machine learning is here to stay and has already had a notable impact on the development of all of the components of the prediction workflow that is visualized in Fig.
Still, the impact of machine learning on weather and climate modeling goes beyond the development of tools to improve prediction systems. Machine learning also has a strong impact on CPU and interconnect technologies, and compute system design. Special machine learning hardware is optimized for dense linear algebra calculations at low numerical precision equal or less than half precision and allows for substantial improvements in performance for applications that can make use of this arithmetic.
While the training and inference of complex machine learning solutions show the best performance on GPU-based systems 84 at the moment, most weather and climate centers still rely on conventional CPU-based systems. While the reduction of precision to three significant decimal digits—as available in IEEE half precision—is challenging but not impossible 90 , no weather and climate model is able to run with less than single precision arithmetic yet.
As tests to use machine learning accelerators within Earth-system models are in their infancy 37 , the weather and climate community is largely unprepared to use hardware optimized for machine learning applications.
On the other hand, the use of machine learning accelerators and low numerical precision comes naturally when using deep-learning solutions within the prediction workflow, in particular if used to emulate and replace expensive model components that would otherwise be very difficult to port to an accelerator, such as the physical parameterization schemes or tangent linear models in data assimilation 91 , Thus, machine learning, and in particular deep learning, also shows the potential to act as a shortcut to HPC efficient code and performance portability.
Box 1 explains the digital-twin concept and its foundation on the continuous fusion of simulations and observations based on information theory. Given these constraints, we focus on a machine and software ecosystem that addresses the extreme-scale aspects of the digital twin most effectively.
For this, we pose three questions: 1 What are the digital-twin requirements? Following the digital-twin definition in Box 1 , its extreme-scale computing requirement is mostly driven by the forecast model itself. Even though the twin is based on a huge ensemble optimization problem using both simulations and observations, its efficiency and scalability is determined by the model. Observation processing and matching observations with model output is comparably cheap.
The optimization procedure itself is mostly based on executing model runs in various forms and performing memory-intensive matrix operations. The digital-twin benchmark would use a very high resolution, coupled Earth-system model ensemble noting that a spatial resolution increase has the largest footprint on computing and data growth When refining the simulation grid by a factor of two in the horizontal dimensions, the computational demand roughly grows by a factor of eight, since doubling the resolution in each of the two spatial dimensions requires a commensurate increase in the number of time steps taken by the simulation.
The ensemble mode multiplies the requirement by as many ensemble members as are required; however, lagged ensembles and using machine learning as a cheaper alternative for characterizing uncertainty 87 can produce substantial efficiency gains.
According to what we covered in the previous sections, a computing and data aware algorithmic framework based on flexible control and data structures can drastically reduce the computing and data footprint. In addition, such framework must overlap the execution of individual model components, focus on stencil operations with little data movement overhead, stretch time steps as much as possible and reduce arithmetic precision.
Machine learning will produce further savings through surrogate models. Apart from producing cost savings, the revised algorithmic framework also facilitates the implementation of more generic software infrastructures making future codes more portable and therefore sustainable. However, it is important to note that implementing high-performance codes in low-level environments is not simple and requires strong human expertise.
We propose a strict separation of concerns of the programming problem into a productive front-end for example, a Python-based domain-specific software framework for the relevant computational patterns and an intermediate representation for example, the multi-level Intermediate Representation MLIR 93 or Stateful DataFlow multi-Graphs SDFG 94 for optimization that can then generate tuned code for the target architectures.
We expect that the design of the front-end will be specialized to our domain or at least to certain computational patterns, while many of the optimizations and transformations on the intermediate representation for example, loop tiling and fusion can be re-used across multiple domains.
Thus, the performance engineering work can utilize existing investments and also benefit from other science disciplines as well as machine learning. A domain-specific, weather and climate architecture design would need to be manufactured in an advanced silicon process to be competitive in terms of energy consumption and performance.
To maximize performance and cost effectiveness it is necessary to use the latest, smallest fabrication processes. While manufacturing costs grow very quickly towards the latest processes, performance grows even faster. As low-cost commoditization only happens at the low-performance end, building high-performance domain-specific architectures today would require a huge market such as deep learning where hundreds of millions of dollar investments can be made.
This means that true weather and climate domain architecture co-design may not be possible unless funding commensurate with the scale of the climate change impact cost would become available.
If we resort to commodity devices that have a large volume market and enable high-performance specialized computations, we are limited to either vectorized CPUs, highly threaded GPUs or reconfigurable FPGAs.
All these devices are manufactured in the latest silicon processes and offer high-performance solutions. Most of the energy in information processing systems is spent moving data between chips or on the chip Only a very small fraction of the energy is actually consumed to perform calculations.
From investigating bounds for stencil programs that are common in weather and climate codes on each of these device types 98 we can conclude that the latest highly vectorized CPUs can be competitive with GPUs if their memory bandwidths match.
Unfortunately, high-bandwidth memory was only recently added to FPGAs so that they will still be outperformed by GPUs in the near future This technological uncertainty also makes it imperative to implement new codes in a performance-portable language, which we suggested already above.
The most competitive architecture for the next years will therefore likely be GPU-accelerated systems for which we need a rough size estimate now. The previously cited benchmark runs used a single, high-resolution model forecast and estimated efficiency gain factors of to comply with the operational one-year-per-day simulation throughput requirement 17 , 18 , Extrapolating this to near-future technology produces an estimate of a remaining shortfall factor of four thus requiring about 20, GPUs to perform the digital-twin calculations with the necessary throughput Table 1.
Several of these systems are already in production to inspire a detailed machine design. An important consideration in machine design is balance. The specific design should be tuned to our domain with an emphasis on data movement over raw floating-point performance given the available hardware at the specific time. An HPC system of sufficient size also creates an environmental footprint that needs to be taken into account.
Performance and efficiency therefore need to make the operation not only economical but also environmentally friendly due to large power consumption rates. The synergy of these developments is summarized as a conceptual view of the entire proposed infrastructure in Fig.
Workflow and algorithmic flexibility are provided by generic control layers and data structures supporting a variety of grid lay-outs, numerical methods and overlapping as well as parallelizing model component process execution and their coupling. Machine learning can deliver both computational efficiency and better physical process descriptions derived from data analytics. Codes follow the separation-of-concerns paradigm whereby front-end, highly legible science code is separated from hardware specific, heavily optimized code back-ends.
The link is provided by a domain-specific software tool-chain. The system architecture maximizes both time and energy to solution and exploits both centralized and cloud-based deployments. It is important to understand that computing hardware and software advance on vastly different time scales. The lifetime of software can be decades while high-performance hardware is usually used for less than five years. The proposed algorithmic and software investments should therefore provide utmost flexibility and openness to new, fast evolving technology.
The digital-twin control layer drives flexible workflows for Earth-system modeling and data assimilation using generic data structures and physical process simulations that exploit parallelism and are based on algorithms minimizing data movement.
DSLs map the algorithmic patterns optimally on the memory and parallel processing power of heterogeneous processor architectures.
The computing architecture is based on heterogeneous, large-scale architectures within federated systems. By how much all these factors will reduce the cost has not yet been fully quantified, but Fig. The optimum system design requires these contributions to be developed together—as they are co-dependent—so that the resulting overall benefit beyond the state of the art can be fully achieved. The distance from the center of the hexagon indicates the magnitude of the individual contributions towards enhanced efficiency for increased spatial resolution, more Earth-system complexity and better uncertainty information provided by ensembles as well as resilient, portable and efficient code and workflow execution, respectively.
Computer system development and innovation never stop. The best price—performance point will quickly shift and in three years, a system design will likely look very different. For example, we could imagine software breakthroughs to happen that will make very low precision arithmetic viable in Earth-system science computations, thus drastically reduce memory and data communication overheads.
Hardware breakthroughs in reconfigurable or spatial as well as analog computing 63 may also become competitive. The societal challenges arising from climate change require a step-change in predictive skill that will not be reachable with incremental enhancements, and the time is ripe for making substantial investments at the interface between Earth-system and computational science to promote the revolution in code design that is described in this paper.
The cost of this effort is small compared to the benefits. Cook, J. Quantifying the consensus on anthropogenic global warming in the scientific literature. Google Scholar. Wallemacq, P. Franco, E. Bauer, P. The quiet revolution of numerical weather prediction. Nature , 47—55 Hausfather, Z. Evaluating the performance of past climate model projections. Sillmann, J.
Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extremes 18 , 65—74 Asch, M. Big data and extreme-scale computing: pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry. High Perform. Khan, H. Platzman, G. Lynch, P. Press, Leutbecher, M. Ensemble forecasting. Zhu, Y. The economic value of ensemble-based weather forecasts.
Palmer, T. The scientific challenge of understanding and estimating climate change. Natl Acad. To obtain a sharing link in OneDrive, highlight the file and choose Get Link in the menu bar. Use a file compression utility Using a compression utility, such as WinZip, creates a compressed archive file that has a different file name extension. Many third-party compression utilities are available. You can right-click any file in Windows 7, Windows 8, or Windows 10 and select Send to compressed zipped folder.
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