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.
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.
1. Introduction to High Performance Computing Architectures
2. High
Performance Computing: Use Cases, APIs and Applications
3. Parallelization
Techniques
4. High Performance Computing for Machine Learning
5.
Implementation of Parallel Computing with Artificial Intelligence in Big Data
Analytics
6. D-UNet: Deep Learning Architecture for Colon Polyp Segmentation
in Endoscopic Images
7. Early-Stage Plant Disease Detection using YOLOv8
8.
Landslide Detection Using Custom Deep Convolutional Neural Network
9. GPU in
Big Data: An Acceleration Technique
10. Use of NLP Techniques and
High-Performance Computing for Automated Knowledge-based Ontology
Construction of Saffron Crop
11. Implementing High-performance Computing with
Artificial Intelligence in Healthcare Systems
12. BLMP2CE: Design of a
Dual-Bioinspired Low-Complexity Data Mining Engine with Parallel Processing
for Automatic Cluster Analysis via Ensemble Learning Operations
13. Deep
Learning and Edge Computing with HPC
14. Usage of IoT, High Performance
Computing, Machine and Deep Learning in a Human Activity Recognition (HAR)
System: Challenges and Opportunities
15. Artificial Intelligence in Industry:
An Approach to Automation
16. Usage of IoT, Artificial Intelligence and
Machine Learning with HPC: Issues, Challenges, and a Case Study
17. Advancing
High-Performance Computing for AI in the Era of Large-Scale Models: A
Research Roadmap
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.
