Parallel and High-Performance Computing in Artificial Intelligence (Hardcover)

Mukesh Raghuwanshi

ISBN 10: 1032540877 / ISBN 13: 9781032540870

Editore: Taylor & Francis Ltd, London, 2025

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Hardcover. 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 books 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 applicationsParallelization techniquesHPC for machine learningImplementation of parallel computing with AI in big data analyticsHPC with AI in healthcare systemsAI in industrial automationCoverage 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 books 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. The book explores high-performance architectures for data-intensive applications, as well as efficient analytical strategies, to speed up data processing in applications in automation, machine learning, deep learning, bioinformatics, natural language processing, and vision intelligence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781032540870

Dati bibliografici

Titolo: Parallel and High-Performance Computing in ...

Casa editrice: Taylor & Francis Ltd, London

Data di pubblicazione: 2025

Legatura: Hardcover

Condizione: new

Descrizione articolo

Riassunto:

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.

Informazioni sull?autore:

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&D Cell Incharge, Jhulelal Institute of Technology, Nagpur.

Dr. Rutvij H. Jhaveri is an experienced researcher working in the Department of Computer Science & Engineering, Pandit Deendayal Energy University (PDEU/PDPU), Gandhinagar, India since Dec. 2019.

Dr. Roshani Raut is an as Associate Professor in the Department of Information Technology and Associate Dean International Relations, in Pimpri Chinchwad College of Engineering, Pune, India.