Using and Customization of Paraview for HPC Post Processing in Michelle

In order to guaranty performances of complex systems using numerical simulation, CEA is performing advanced data analysis and scientific visualization with open source software using High Performance Computing (HPC) capability. The diversity of the physics to study produces results of growing complexity in terms of large-scale, high dimensional and multivariate data. Moreover, the HPC approach introduces another layer of complexity by allowing computation amongst thousands of remote cores accessed from sites located hundreds of kilometers away from the computing facility. This paper presents how CEA deploys and contributes to open source software to enable production class visualization tools in a high performance computing context. Among several open source projects used at CEA, this presentation will focus on Visit, VTK and Paraview. In the first part we will address specific issues encountered when deploying VisIt and Paraview in a multi-site supercomputing facility for end-users. Several examples will be given on how such tools can be adapted to take advantage of a parallel setting to explore large multi-block dataset or perform remote visualization on material interface reconstructions of billions of cells. Then, the specific challenges faced to deliver Paraview’s Catalyst capabilities to end-users will be discussed. In the second part, we will describe how CEA contributes to open source visualization software and associated software development strategy by emphasizing on two recent development projects. The first is an integrated simulation workbench providing plugins for every step required to achieve numerical simulation independently on a local or a remote computer. Embedded in an Eclipse RCP environment, VTK views allow the users to perform data input using interaction or mesh preview before running the simulation code. Contributions to VTK have been made in order to smoothly integrate these technologies. The second details how recent developments at CEA have helped to visualize and to analyze results from ExaStamp, a parallel molecular dynamics simulation code dealing with molecular systems ranging from a few millions up to a billion atoms. These developments include a GPU intensive rendering method specialized for atoms and specific parallel algorithms to process molecular data sets.

The Supercomputing Education and Research Centre (SERC) at the Indian Institute of Science (IISc), located in the South East Asian Region, has been providing state-of-the-art services and support for High Performance Computing (HPC) for the academic users of the institute since 1990. This centre, facilitates OpenMP, MPI, CUDA and OpenCL based HPC jobs for academic research. To augment or support the existing demand for compute intensive, tightly coupled HPC workloads in the institute, it was desirable to explore cloud based HPC services. This study is a comparative cost-benefit analysis of the in-house compute facility at SERC with that of the commercial cloud providers like Amazon, Google Cloud, Microsoft Azure and Sabalcore. The Total Cost of Ownership (TCO) of SahasraT (the in-house supercomputer) is computed and used to identify the cost of running a small job for 24 hours on the in-house facility. This is then compared with the estimated cost of running the same job on comparative cloud instances. Several research papers that provide a detailed evaluation of HPC on the cloud with performance benchmarks and the comparison to in-house HPC facilities, were studied. These references have been used to compare performance of compute instance in the public cloud to the performance of HPC at SahasraT. The results obtained in this study, advocate that current cloud platforms are expensive in both cost and performance as compared to the in-house facility at SERC

Space station static and dynamic analyses using parallel methods

Sandia National Laboratories has a long history of significant contributions to the high performance community and industry. Our innovative computer architectures allowed the United States to become the first to break the teraFLOP barrier—propelling us to the international spotlight. Our advanced simulation and modeling capabilities have been integral in high consequence US operations such as Operation Burnt Frost. Strong partnerships with industry leaders, such as Cray, Inc. and Goodyear, have enabled them to leverage our high performance computing (HPC) capabilities to gain a tremendous competitive edge in the marketplace. As part of our continuing commitment to providing modern computing infrastructure and systems in support of Sandia missions, we made a major investment in expanding Building 725 to serve as the new home of HPC systems at Sandia. Work is expected to be completed in 2018 and will result in a modern facility of approximately 15,000 square feet of computer center space. The facility will be ready to house the newest National Nuclear Security Administration/Advanced Simulation and Computing (NNSA/ASC) Prototype platform being acquired by Sandia, with delivery in late 2019 or early 2020. This new system will enable continuing advances by Sandia science and engineering staff in the areas of operating system R&D, operation cost effectiveness (power and innovative cooling technologies), user environment and application code performance.

Molten Salt Reactors display a strong coupling between neutronics and thermal-hydraulics for which various simulation tools have been developed. In this paper we describe TRUST-NK, a neutronics code based on TRUST, CEA’s High Performance Computing (HPC) platform. TRUST-NK provides a multi-group diffusion solver with transport of delayed neutron precursors, for both steady-state and transient simulations. Being based on TRUST allows TRUST-NK to take advantage of the platform’s HPC capabilities and to easily couple to CFD solvers provided by other derivatives of TRUST. After a brief description of the code, we present an example of application to a simplified MSR model.

Modern High Performance Computing (HPC) sys-tems are built with innovative system architectures and novel programming models to further push the speed limit of computing. The increased complexity poses challenges for performance portability and performance evaluation. The Standard Perfor-mance Evaluation Corporation (SPEC) has a long history of producing industry-standard benchmarks for modern computer systems. SPEC's newly released SPEChpc 2021 benchmark suites, developed by the High Performance Group, are a bold attempt to provide a fair and objective benchmarking tool designed for state-of-the-art HPC systems. With the support of multiple host and accelerator programming models, the suites are portable across both homogeneous and heterogeneous architectures. Different workloads are developed to fit system sizes ranging from a few compute nodes to a few hundred compute nodes. In this work we present our first experiences in performance benchmarking the new SPEChpc2021 suites and evaluate their portability and basic performance characteristics on various popular and emerging HPC architectures, including x86 CPU, NVIDIA GPU, and AMD GPU. This study provides a first-hand experience of executing the SPEChpc 2021 suites at scale on production HPC systems, discusses real-world use cases, and serves as an initial guideline for using the benchmark suites.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

<p>The objective of this study is to integrate a continuum-based deformable tire and terrain interaction model into a general-use physics-based simulation environment capable of off-road vehicle mobility analysis and high-performance computing potential. Specifically, the physics-based deformable tire and terrain models which were recently proposed and validated by Yamashita, et al. will be implemented into the structure of the multi-physics simulation engine Chrono. In off-road vehicle mobility analysis, empirical and analytical models have been commonly used for vehicle-terrain interaction. While these models utilize experimental data or terramechanics theories to create quick predictive mobility models, they are unable to capture the highly nonlinear behavior of soft soil deformation, which can lead to inaccurate or unreliable results. In order to resolve these limitations, the use of physics-based numerical approaches have been proposed. These methods make use of finite element and discrete element simulations to describe the interaction between the vehicle and deformable terrain. Continuum-based finite element models transfer tire forces to the terrain and model the deformation with elasto-plastic constitutive models. Discrete element soil uses a large number of small rigid body particles to describe the microscale behavior of granular terrain, with the deformation of the soil represented by the motion and contact of the particles. While these physics-based models offer a more accurate vehicle-terrain interaction model, the solution procedure can become complex and computationally expensive since co-simulation techniques are often used.</p><p>To address these issues, the analysis of physics-based full vehicle dynamics simulations utilizing high-fidelity deformable tire and terrain models in a multi-physics engine with high-performance computing capability is desired. To this end, a continuum mechanics based shear deformable laminated composite shell element proposed by Yamashita, et al. was integrated into the flexible body dynamics simulation framework of Chrono. This element was based on the absolute nodal coordinate formulation and is defined by the global position coordinates and the transverse gradient coordinates of its four nodes. Element lockings are eliminated with the incorporation of the enhanced assumed strain (EAS) and assumed natural strain approaches (ANS). The element formulation includes an extension to model laminated composite materials. Additionally, a locking-free 9-node brick element was integrated into the Chrono framework that makes use of the curvature coordinates at the center of the element. This element is formulated with the Hencky strain measure such that multiplicative finite strain plasticity theory can be used to incorporate soil plasticity models, such as the capped Drucker-Prager failed criterion.</p><p>With the shear deformable laminated composite shell element and plastic soil brick element integrated into the Chrono multi-physics simulation engine, an off-road deformable tire and terrain interaction model was developed using the vehicle dynamics simulation module Chrono::Vehicle. An off-road deformable tire model was parameterized based on commercial tire properties and generated as an interchangeable tire model option in the full vehicle dynamics system. Benchmark verification tests were performed to ensure the accuracy of tire deformation and tire force characteristics. Further tests were performed to validate a deformable tire model with a deformable tread pattern constructed from shear deformable shell elements and co-rotational tetrahedral elements. The deformable soil model was also integrated as a terrain option in Chrono::Vehicle and numerical tests were carried out to demonstrate its interaction with rigid and deformable tire models. To make use of the computational performance enhancements available in Chrono, Open Multi-Processing (OpenMP) and Advanced Vector Extensions (AVX) were applied to the evaluation of the elastic force/Jacobian matrix and large matrix operations of flexible bodies, respectively, in order to reduce the computation time by nearly 60%.</p>

Power monitoring system for university buildings: Architecture and advanced analysis tools

Parallel computing has become fundamental in processing the massive data volumes generated in modern science and industry. This paper presents a comprehensive survey and practical review of parallel computing in data processing, examining key frameworks (MPI, Open MP, CUDA, Hadoop MapReduce, Apache Spark, etc.), implementations, and real- world case studies. We discuss the architectures underlying shared-memory and distributed-memory systems and illustrate how parallelism on CPUs and GPUs is exploited for high- performance computing (HPC) and big data analytics. We review theoretical foundations (including Amdahl’s law for speedup limits) and compare programming models in terms of design, performance, and fault tolerance. A two-column IEEE-style for- mat is used to present critical insights from scholarly literature, with diagrams illustrating parallel architectures and workflows, and tabular summaries of tools and results. Case studies—from scientific HPC simulations to large-scale data analytics—highlight practical parallelism on multicore CPUs, GPUs, and computing clusters. We conclude with a discussion on the convergence of HPC and big data paradigms, the challenges of heterogeneous computing, and emerging trends. This survey serves as a reference for PhD researchers and practitioners seeking to understand frameworks and real-world practices in parallel computing for data processing.

Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

Chapter 4 - Benchmarking

The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.

High-Performance Computing, HPC, has become one of the most active computer science fields. Driven mainly by the need for high processing capabilities required by algorithms from many areas, such as Big Data, Artificial Intelligence, Data Science, and subjects related to chemistry, physics, and biology, the state-of-art algorithms from these fields are notoriously demanding computer resources. Therefore, choosing the right computer system to optimize their performance is paramount. This article presents the main challenges of future supercomputer systems, highlighting the areas that demand the most of HPC servers; the new architectures, including heterogeneous processors composed of artificial intelligence chips, quantum processors, the adoption of HPC on cloud servers; and the challenges of software developers when facing parallelizing applications. We also discuss challenges regarding non-functional requirements, such as energy consumption and resilience.

The power grid is becoming far more complex as a result of the grid evolution meeting an information revolution. Due to the penetration of smart grid technologies, the grid is evolving as an unprecedented speed and the information infrastructure is fundamentally improved with a large number of smart meters and sensors that produce several orders of magnitude larger amounts of data. How to pull data in, perform analysis, and put information out in a real-time manner is a fundamental challenge in smart grid operation and planning. The future power grid requires high performance computing to be one of the foundational technologies in developing the algorithms and tools for the significantly increased complexity. New techniques and computational capabilities are required to meet the demands for higher reliability and better asset utilization, including advanced algorithms and computing hardware for large-scale modeling, simulation, and analysis. This chapter summarizes the computational challenges in smart grid and the need for high performance computing, and present examples of how high performance computing might be used for future smart grid operation and planning.