Assimilation of remote sensing into crop growth models: Current status and perspectives

. The best hope for reducing long-standing global climate model biases is by
increasing resolution to the kilometer scale. Here we present results from an
ultrahigh-resolution non-hydrostatic climate model for a near-global setup
running on the full Piz Daint supercomputer on 4888 GPUs (graphics
processing units). The dynamical core of the model has been completely
rewritten using a domain-specific language (DSL) for performance portability
across different hardware architectures. Physical parameterizations and
diagnostics have been ported using compiler directives. To our knowledge this
represents the first complete atmospheric model being run entirely on
accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve
a simulation throughput of 0.043 (0.23) simulated years per day and an energy
consumption of 596 MWh per simulated year. Furthermore, we propose a new
memory usage efficiency (MUE) metric that considers how efficiently the
memory bandwidth – the dominant bottleneck of climate codes – is being
used.

/pdf/near-global-climate-simulation-at-1-km-resolution-4ozadt8hrz.pdf

Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0

Over the last 15 years, significant changes have occurred in the areas of electric power systems and high performance computing (HPC). HPC has seen the maturation of cluster computing, the advent of multi-core computing, the creation of grid and cloud computing, and the sudden rise of the graphics processing unit (GPU) for general purpose computing. These changes have also been coupled with the slow ending of Moore's law. Electric power systems have also undergone many changes including the introduction of the advanced metering infrastructure and other advanced technologies for data collection, the inclusion of renewable/distributed power generation, and the addition of electric vehicles as stochastic loads. Clear goals have also been set for the development of the smart grid-a unique cyber-physical system (CPS) that requires the interaction of the electric power system and HPC. As such, this work explores the many ways in which HPC will be used in the smart grid and its CPS in the future including real-time and off-line analysis, data mining/storage, intelligent coordination, security, simulation, and visualization.

Applications and Trends of High Performance Computing for Electric Power Systems: Focusing on Smart Grid

Photonic neuromorphic computing is raising a growing interest as it promises to provide massive parallelism and low power consumption. In this paper, we demonstrate for the first time a feed-forward neural network via an 8 × 8 Indium Phosphide cross-connect chip, where up to 8 on-chip weighted addition circuits are co-integrated, based on semiconductor optical amplifier technology. We perform the weight calibration per neuron, resulting in a normalized root mean square error smaller than 0.08 and a best case dynamic range of 27 dB. The 4 input to 1 output weighted addition operation is executed on-chip and is part of a neuron, whose non-linear function is implemented via software. A three feedback loop optimization procedure is demonstrated to enable an output neuron accuracy improvement of up to 55%. The exploitation of this technology as neural network is evaluated by implementing a trained 3-layer photonic deep neural network to solve the Iris flower classification problem. Prediction accuracy of 85.8% is achieved, with respect to the 95% accuracy obtained via a computer. A comprehensive analysis of the error evolution in our system reveals that the electrical/optical conversions dominate the error contribution, which suggests that an all optical approach is preferable for future neuromorphic computing hardware design.

Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect

Duality Cache is an in-cache computation architecture that enables general purpose data parallel applications to run on caches. This paper presents a holistic approach of building Duality Cache system stack with techniques of performing in- cache floating point arithmetic and transcendental functions, enabling a data-parallel execution model, designing a compiler that accepts existing CUDA programs, and providing flexibility in adopting for various workload characteristics. Exposure to massive parallelism that exists in the Duality Cache architecture improves performance of GPU benchmarks by 3.6x and OpenACC benchmarks by 4.0x over a server class GPU. Re-purposing existing caches provides 72.6x better performance for CPUs with only 3.5% of area cost. Duality Cache reduces energy by 5.8x over GPUs and 21x over CPUs.

https://dl.acm.org/doi/pdf/10.1145/3307650.3322257

Duality cache for data parallel acceleration

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect fo...

Debunking the 100X GPU vs. CPU myth

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Debunking the 100X GPU vs. CPU myth