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Parallel and High-Performance Computing in Artificial Intelligence

Edited by Mukesh Raghuwanshi, Edited by Pradnya Borkar, Edited by Rutvij H. Jhaveri, Edited by Roshani Raut

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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 bookâ€™s two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include: HPC use cases, 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 bookâ€™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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