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We describe the underlying mathematics, validation, and applications of a novel Helmholtz free-energy—minimizing phase-field model solved within the framework of the lattice Boltzmann method (LBM) for efficiently simulating two-phase pore-scale flow directly on large 3D images of real rocks obtained from micro-computed tomography (micro-CT) scanning. The code implementation of the technique, coined as the eLBM (energy-based LBM), is performed in CUDA programming language to take maximum advantage of accelerated computing by use of multinode general-purpose graphics processing units (GPGPUs). eLBM’s momentum-balance solver is based on the multiple-relaxation-time (MRT) model. The Boltzmann equation is discretized in space, velocity (momentum), and time coordinates using a 3D 19-velocity grid (D3Q19 scheme), which provides the best compromise between accuracy and computational efficiency. The benefits of the MRT model over the conventional single-relaxation-time Bhatnagar-Gross-Krook (BGK) model are (I) enhanced numerical stability, (II) independent bulk and shear viscosities, and (III) viscosity-independent, nonslip boundary conditions. The drawback of the MRT model is that it is slightly more computationally demanding compared to the BGK model. This minor hurdle is easily overcome through a GPGPU implementation of the MRT model for eLBM. eLBM is, to our knowledge, the first industrial grade–distributed parallel implementation of an energy-based LBM taking advantage of multiple GPGPU nodes. The Cahn-Hilliard equation that governs the order-parameter distribution is fully integrated into the LBM framework that accelerates the pore-scale simulation on real systems significantly. While individual components of the eLBM simulator can be separately found in various references, our novel contributions are (1) integrating all computational and high-performance computing components together into a unified implementation and (2) providing comprehensive and definitive quantitative validation results with eLBM in terms of robustness and accuracy for a variety of flow domains including various types of real rock images. We successfully validate and apply the eLBM on several transient two-phase flow problems of gradually increasing complexity. Investigated problems include the following: (1) snap-off in constricted capillary tubes; (2) Haines jumps on a micromodel (during drainage), Ketton limestone image, and Fontainebleau and Castlegate sandstone images (during drainage and subsequent imbibition); and (3) capillary desaturation simulations on a Berea sandstone image including a comparison of numerically computed residual non-wetting-phase saturations (as a function of the capillary number) to data reported in the literature. Extensive physical validation tests and applications on large 3D rock images demonstrate the reliability, robustness, and efficacy of the eLBM as a direct visco-capillary pore-scale two-phase flow simulator for digital rock physics workflows.

Landslide-induced tsunami is a complex fluid–solid coupling process that plays a crucial role in the study of a disaster chain. To simulate the coupling behaviors between the fluid and solid, a graphics processing unit-based coupled smoothed particle hydrodynamics (SPH)-discrete element method (DEM) code is developed. A series of numerical tests, which are based on the laboratory test by Koshizuka et al. (Particle method for calculating splashing of incompressible viscous fluid, 1995) and Kleefsman et al. (J Comput Phys 206:363–393, 2005), are carried out to study the influence of the parameters, and to verify the accuracy of the developed SPH code. To ensure accurate results of the SPH simulation, the values for the diffusion term, particle resolution (1/25 characteristic length), and smoothing length (1.2 times of particle interval) are suggested. The ratio of the SPH particle size and the DEM particle’s diameter influences the accuracy of the coupling simulation between solid particles and water. For the coupling simulation of a single particle or a loose particle assembly (not contact each other) with fluid, this ratio should be smaller than 1/20; for a dense particle assembly, a ratio of smaller than 1/6 will be good.

The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.

Digital rock physics (DRP) is a rapidly evolving technology targeting fast turnaround times for repeatable core analysis and multi-physics simulation of rock properties. We develop and validate a rapid and scalable distributed-parallel single-phase pore-scale flow simulator for permeability estimation on real 3D pore-scale micro-CT images using a novel variant of the lattice Boltzmann method (LBM). The LBM code implementation is designed to take maximum advantage of distributed computing on multiple general-purpose graphics processing units (GPGPUs). We describe and extensively test the distributed parallel implementation of an innovative LBM algorithm for simulating flow in pore-scale media based on the multiple-relaxation-time (MRT) model that utilizes a precise treatment of body force. While the individual components of the resulting simulator can be separately found in various references, our novel contributions are (1) the integration of all of the mathematical and high-performance computing components together with a highly optimized code implementation and (2) the delivery of quantitative results with the simulator in terms of robustness, accuracy, and computational efficiency for a variety of flow geometries including various types of real rock images. We report on extensive validations of the simulator in terms of accuracy and provide near-ideal distributed parallel scalability results on large pore-scale image volumes that were largely computationally inaccessible prior to our implementation. We validate the accuracy of the MRT-LBM simulator on model geometries with analytical solutions. Permeability estimation results are then provided on large 3D binary microstructures including a sphere pack and rocks from various sandstone and carbonate formations. We quantify the scalability behavior of the distributed parallel implementation of MRT-LBM as a function of model type/size and the number of utilized GPGPUs for a panoply of permeability estimation problems.

Ranking aggregation, studied in the field of social choice theory, focuses on the combination of information with the aim of determining a winning ranking among some alternatives when the preferences of the voters are expressed by ordering the possible alternatives from most to least preferred. One of the most famous ranking aggregation methods can be traced back to 1959, when Kemeny introduces a measure of distance between a ranking and the opinion of the voters gathered in a profile of rankings. Using this, he proposed to elect as winning ranking of the election the one that minimizes the distance to the profile. This is factorial on the number of alternatives, posing a handicap in the runtime of the algorithms developed to find the winning ranking, which prevents its use in real problems where the number of alternatives is large. In this work we introduce the first algorithm for the Kemeny problem designed to be executed in a Graphical Processing Unit. The threads identifiers are codified to be associated with rankings by means of the factorial number system, a radix numeral system that is then used to uniquely pair a ranking with the thread using Lehmer’s code. Results guarantee constant execution time up to 14 alternatives.

A two-phase flow model accelerated by graphical processing unit (GPU) is developed to solve fluid-solid interaction (FSI) using the sharp-interface immersed boundary method (IBM). This model solves the incompressible Navier-Stokes equations using the projection-based fractional step method in a fixed staggered Cartesian grid system. A volume of fluid (VOF) method with second-order accuracy is employed to trace the free surface. To represent the intricate surface geometry, the structure is discretized using the unstructured triangle mesh. Additionally, a ray tracing method is employed to classify fluid and solid points. A high-order stable scheme has been introduced to reconstruct the local velocity at interface points. Three FSI problems, including wave evolution around a breakwater, interaction between a periodic wave train and a moving float, and a 3-D moving object interacting with the free surface, were investigated to validate the accuracy and stability of the proposed model. The numerical results are in good agreement with the experimental data. Additionally, we evaluated the computational performance of the proposed GPU-based model. The GPU-based model achieved a 42.29 times speedup compared with the single-core CPU-based model in the three-dimension test. Additionally, the results regarding the time cost of each code section indicate that achieving more significant acceleration is associated with solving the turbulence, advection, and diffusion terms, while solving the pressure Poisson equation (PPE) saves the most time. Furthermore, the impact of grid number on computational efficiency indicates that as the number of grids increases, the GPU-based model outperforms the multi-core CPU-based model.

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.

Biomedical named entity recognition is a popular research topic in the Biosciences domain as number of biomedical articles getting published are increasing rapidly. Generic models using machine learning and deep learning techniques have been proposed for extracting these entities in the past, however there is no clear verdict on which techniques are better and how these generic models perform in a domain-specific big data scenario. In this paper, we evaluate three baseline models using the most complex BioNLP 2013 cancer genetics dataset addressing the cancer domain. A classifier ensemble, bidirectional long short-term memory (Bi-LSTM) model and the bidirectional encoder representations from transformers (BERT) model are implemented. We propose NeRBERT, a domain-specific, graphical processing unit (GPU) pre-trained language model using extra biomedical corpora extending BERTBASE. Experimental results prove the efficacy of NeRBERT as it outperforms the other three models with an F1-score gain of 12.18 pp, 8.59 pp and 5.43 pp over the ensemble, Bi-LSTM and BERT models respectively. GPUs reduce the model training time to less than half. Comparing it to existing state-of-the-art models, it performs 1.57 pp higher than the next best existing model compared, emerging as a robust biomedical and cancer phenotyping NER tagger.

This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson’s dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts.

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks (ANNs) leveraging modern graphics processing units (GPUs) have enabled the rapid analysis of structured input data – sequences, images, videos – to predict complex outputs with unprecedented accuracy. While there have been early successes in flow cytometry, for example, the extensive potential of pairing microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges remains largely untapped. Here we provide a roadmap to integrating deep learning and microfluidics in biotechnology laboratories that matches computational architectures to problem types, and provide an outlook on emerging opportunities. High-throughput microfluidics has revolutionized biotechnology assays, enabling intriguing new approaches often at the single-cell level. Combining deep learning (to analyze data) with microfluidics (to acquire data) represents an emerging opportunity in biotechnology that remains largely untapped. Deep learning architectures have been developed to tackle raw structured data and address problems common to microfluidics applications in biotechnology. With the abundance of open-source training materials and low-cost graphics processing units, the barriers to entry for microfluidics labs have never been lower.