High performance computing artificial (91 Ergebnisse)

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Paperback or Softback. Zustand: New. High-Performance Computing and Artificial Intelligence in Process Engineering. Book.

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Taschenbuch. Zustand: Neu. Neuware - High-performance computing (HPC) and artificial intelligence (AI) in process engineering involve complex system modelling, data analysis, optimization design, and real-time monitoring. Key methods include data integration, model construction, optimization algorithms, machine learning, deep learning, parallel computing, and real-time analytics. These techniques significantly enhance production efficiency, reduce costs, and improve system stability. They also promote industrial intelligence, creating new opportunities and challenges in process engineering. This integration supports the advancement of Industry 4.0 and smart manufacturing.Key Features: - Provides a systematic review of state-of-the-art artificial intelligence technologies and high-performance computing, and their applications in process engineering - Introduces the development of traditional process simulators in process engineering and new numerical solvers based on data-driven and physics-informed neural networks approaches - Provides perspectives of high-performance computing and artificial intelligence from industrial leaders in software and hardware - Aimed at researchers and industrial practitioners in process engineering, manufacturing, data science, artificial intelligence and high-performance computing.…

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Zustand: New. Dr. M. M. Raghuwanshi is the Dean of Engineering at S.B.Jain Institute of Technology Management and Research, Nagpur, India.Dr. Pradnya Borkar is an Associate Professor at the Department of Computer Science and Engineering and R&amp.

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Gebundene Ausgabe. Zustand: Neu. In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Subsequently, Huang et al. proposed a named entity recognition method for geological news based on BERT model. Yang et al. proposed an automatic landslide identification method to solve the problem of the time-consuming nature and low efficiency of traditional landslide identification methods. Du et al. analyzed the potential of unsupervised machine learning methods for submarine landslide prediction. Wang et al. performed parallel computations on the inversion algorithm of the two-dimensional ZTEM. Xu et al. used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides. Furthermore, Cao et al. proposed a new method called dual encoder transform (DualET) for the short-term prediction of photovoltaic power. Hao et al. conducted a series of parallel optimizations and large-scale parallel simulations on the high-resolution ocean model. Wang et al. proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion. Finally, Yang et al. developed a measure of site-level gross primary productivity (GPP) using the GeoMAN model. Sprache: Englisch Gewicht in Gramm: 613.…

Taschenbuch. Zustand: Neu. Neuware - Parallel and High-Performance Computing in Artificial Intelligence explores high-performance architectures for data-intensive applications as well as efficient analytical strategies to speed up data processing and applications in automation, machine learning, deep learning, healthcare, bioinformatics, natural language processing (NLP), and vision intelligence.The book's two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include: - HPC use cases, application programming interfaces (APIs), and applications - Parallelization techniques - HPC for machine learning - Implementation of parallel computing with AI in big data analytics - HPC with AI in healthcare systems - AI in industrial automation Coverage of HPC architecture and techniques includes multicore architectures, parallel-computing techniques, and APIs, as well as dependence analysis for parallel computing. The book also covers hardware acceleration techniques, including those for GPU acceleration to power big data systems.As AI is increasingly being integrated into HPC applications, the book explores emerging and practical applications in such domains as healthcare, agriculture, bioinformatics, and industrial automation. It illustrates technologies and methodologies to boost the velocity and scale of AI analysis for fast discovery. Data scientists and researchers can benefit from the book's discussion on AI-based HPC applications that can process higher volumes of data, provide more realistic simulations, and guide more accurate predictions. The book also focuses on deep learning and edge computing methodologies with HPC and presents recent research on methodologies and applications of HPC in AI. …

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Gebundene Ausgabe. Zustand: Neu. High-Performance Computing: Architectures, Parallel Programming, and Intelligent Applications offers a comprehensive exploration of the technologies, architectures, and intelligent applications that shape modern high-performance computing. As computational workloads continue to expand across Artificial Intelligence, Machine Learning, scientific research, engineering, healthcare, finance, climate modeling, and big data analytics, HPC has become essential for solving complex problems rapidly and efficiently.The book introduces the fundamental concepts of HPC, including performance measurement, scalability, speedup, efficiency, parallelism, and workload optimization. It explores modern computing architectures, including multicore and many-core processors, GPUs, heterogeneous systems, shared-memory and distributed-memory architectures, HPC clusters, supercomputers, and emerging exascale platforms. A significant focus is placed on parallel programming and optimization. Readers are introduced to MPI, OpenMP, Pthreads, CUDA, OpenCL, GPU programming, synchronization, communication, load balancing, memory management, and performance optimization techniques. The book also covers distributed computing, cluster scheduling, grid computing, cloud-based HPC, virtualization, containerization, resource management, fault tolerance, and energy-efficient computing. The book further connects HPC with Artificial Intelligence, Distributed AI, and HPC-enabled intelligent applications. Sprache: Englisch Gewicht in Gramm: 547.…