High-Performance Parallel Computing for Scientific Simulations

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

A 34‐question survey was sent to researchers in chemistry, physics, engineering and environmental sciences, with the aim of identifying their relationship with unsuccessful research results as well as the obstacles to and incentives for their dissemination. Most of the 310 participants were affiliated with French research and teaching institutions. Conducted within the framework of the Datacc project (datacc.org) on research and data management, supported by Lyon and Grenoble Alpes University Libraries, our study fills the gap in literature on the disciplines concerned with reporting negative results in scholarly research. It shows that 81% of the researchers surveyed have already produced relevant negative results and 75% say they are willing to publish this type of data. However, only 12.5% of respondents have had the opportunity to do so in a scientific journal. This gaping contrast between intention and practice raises questions about the barriers and potential solutions.

Childhood lead poisoning in China: challenges and opportunities.

Biotechnology and BioengineeringVolume 23, Issue 11 p. 2641-2645 Communications To The EditorFree Access Production of cellulase from trichoderma reesei in fed-batch fermentation from soluble carbon sources A. L. Allen, A. L. Allen Enzyme and Biochemical Engineering Group Environmental Sciences and Engineering Division Science and Advanced Technology Laboratory US Army Natick Research and Development Laboratories Natick, Massachusetts 01760Search for more papers by this authorR. E. Mortensen, R. E. Mortensen Enzyme and Biochemical Engineering Group Environmental Sciences and Engineering Division Science and Advanced Technology Laboratory US Army Natick Research and Development Laboratories Natick, Massachusetts 01760Search for more papers by this author A. L. Allen, A. L. Allen Enzyme and Biochemical Engineering Group Environmental Sciences and Engineering Division Science and Advanced Technology Laboratory US Army Natick Research and Development Laboratories Natick, Massachusetts 01760Search for more papers by this authorR. E. Mortensen, R. E. Mortensen Enzyme and Biochemical Engineering Group Environmental Sciences and Engineering Division Science and Advanced Technology Laboratory US Army Natick Research and Development Laboratories Natick, Massachusetts 01760Search for more papers by this author First published: November 1981 https://doi.org/10.1002/bit.260231119Citations: 34AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume23, Issue11November 1981Pages 2641-2645 ReferencesRelatedInformation

Here we synthesise the results of three participatory workshops to explore sustainable development priorities in eastern Africa, and discuss these in the context of Earth and environmental science. The planet is a core pillar of sustainable development, and the engagement of Earth and environmental scientists is vital to achieving the UN Sustainable Development Goals (SDGs). In 2017, the British Geological Survey gathered 76 delegates from 48 organisations at three workshops in Nairobi (Kenya), Lusaka (Zambia), and Dar es Salaam (Tanzania). Using the SDGs as a reference tool, participants (i) identified development priorities at regional (eastern Africa) and national scales, (ii) explored the Earth and environmental science research and data needs to help address these, and (iii) co-designed relevant science-for-development projects. Participants identified sustainable development priorities to be basic (or immediate) needs, including zero hunger (SDG 2), education (SDG 4), ending poverty (SDG 1), and water and sanitation (SDG 6). Participants also described examples of Earth and environmental science research, training, technologies, monitoring and management to support sustainable development. Emerging themes included environmental data (collection, management, integration, access), policy and regulations (integrating environmental science, and policy coherence), resource management (degradation, pollution and environmental protection), and scientific education and understanding (training, knowledge exchange, public understanding of science). A comparative synthesis of existing regional and national development strategies indicates that current narratives of development interventions do not fully capture the opportunities from environmental data integration and policy coherence. Greater engagement with and by the Earth and environmental science community could help to advance these themes to support sustainable development in eastern Africa. This would support efforts to reduce environmental degradation, improve natural resource management, and inform the utilisation of natural resources to improve economic growth and social wellbeing.

Physical and numerical modelling of air-water flows: An Introductory Overview

This edited book aims to present the state of the art in research and development of the convergence of high-performance computing and parallel programming for various engineering and scientific applications. The book has consolidated algorithms, techniques, and methodologies to bridge the gap between the theoretical foundations of academia and implementation for research, which might be used in business and other real-time applications in the future.The book outlines techniques and tools used for emergent areas and domains, which include acceleration of large-scale electronic structure simulations with heterogeneous parallel computing, characterizing power and energy efficiency of a data-centric high-performance computing runtime and applications, security applications of GPUs, parallel implementation of multiprocessors on MPI using FDTD, particle-based fused rendering, design and implementation of particle systems for mesh-free methods with high performance, and evolving topics on heterogeneous computing. In the coming days the need to converge HPC, IoT, cloud-based applications will be felt and this volume tries to bridge that gap.

This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air quality deterioration poses significant risks to both human health and ecological equilibrium, pinpointing relevant features becomes essential for boosting prediction accuracy. The scope of the research includes comparing the performance of mWOA with the original WOA in two feature selection techniques: filter-based and wrapper-based. The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. The statistical results demonstrate the effectiveness of the proposed mWOA approach, outperforming WOA due to the modified control parameter that enables a more precise exploration of the search area. The findings of this study reveal the improved performance of mWOA in selecting informative features, resulting in better prediction on average: 93.75% for filter-based and 94.49% for wrapper-based. In conclusion, the wrapper-based feature selection using the mWOA approach proves to be a valuable asset in enhancing the accuracy and efficiency of ozone level detection models. In the future, the proposed technique can be used for more applications in environmental science and engineering research.

Synchrotron radiation (SR) research at Cornell University is presently carried out at the Cornell High Energy Synchrotron Source (CHESS), a National X-ray Facility on the Cornell Electron/Positron Storage Ring (CESR), a machine mainly dedicated to X-ray production and supported by the National Science Foundation (NSF). Each year, over a thousand scientists and students use a dozen X-ray stations for multidisciplinary research in physics, chemistry, biology, environmental and materials sciences, and engineering on a machine typically operating with compact undulators at 5.3 GeV and 100 to 200 mA.

How much and where do Mexican scientists publish their research in environmental science?

The effects of shielding the tips of airfoils

Simulating a supersonic wake flow field behind a conical body is a computing intensive task. It requires a large number of computational cells to capture the dominant flow physics and a robust numerical algorithm to obtain a reliable solution. High performance parallel computers with unique distributed processing and data storage capability can provide this need. They have larger computational memory and faster computing time than conventional vector computers. We apply the PINCA Navier-Stokes code to simulate a wind-tunnel supersonic wake experiment on Intel Gamma, Intel Paragon, and IBM SP2 parallel computers. These simulations are performed to study the mean flow in the near wake region of a sharp, 7-degree half-angle, adiabatic cone at Mach number 4.3 and freestream Reynolds number of 40,600. Overall the numerical solutions capture the general features of the hypersonic laminar wake flow and compare favorably with the wind tunnel data. With a refined and clustering grid distribution in the recirculation zone, the calculated location of the rear stagnation point is consistent with the 2D axisymmetric and 3D experiments. In this study, we also demonstrate the importance of having a large local memory capacity within a computer node and the effective utilization of the number of computermore » nodes to achieve good parallel performance when simulating a complex, large-scale wake flow problem.« less

Quantitative Aspects of the Economic Growth of Nations: VIII. Distribution of Income by Size

The knapsack problem is a typical one of NPC problems, which is easy to be described but difficult to be solved. It is very important in theory and practice to study it. Nowadays there is a variety of research in algorithm for solving it. As the parallel processing technologies develop, the research of effective parallel algorithms for this problem attracts much attention. To run those algorithms needs high level hardware and high performance parallel computers. Using mobile agent technologies, a more effective model to solve the complex distributed problems can be established. Combines mobile agent with the traditional parallel algorithm, the process in a parallel computer can be evolved into the one performed by some ordinary computers. This can avoid the limitation of experiment conditions and provides convenience in practice. In this paper, a distributed algorithm is proposed for the 0-1 knapsack problem based on the mobile agent, and it is feasible and effective in theoretical analysis.

Ecological momentary assessment (EMA) is a widely used methodology in psychological sciences; however, more broadly, environmental scientists have yet to fully capitalize on the benefits this method offers for gaining a critical understanding of subjective and behavioral responses to environmental factors. EMA enables the collection of experiences and actions occurring in one's natural environment as they unfold over time, allowing researchers to gain contextually informed, dynamic, and longitudinal insights. EMA can provide an accurate understanding of experiences and behaviors relevant to environmental science. To share this perspective, first, we describe current limitations in environmental research that could be addressed through the integration of EMA. Second, we discuss several benefits of adopting EMA in environmental sciences. Finally, we highlight the challenges and considerations involved in integrating EMA. The overarching implication of this work is to foster the interdisciplinary potential and promise of EMA methodology in advancing environmental science research.