Graph processing involves the manipulation, analysis, and traversal of graph data structures. Graphs consist of vertices/nodes connected by edges/links, representing relationships between entities. Graph processing is crucial in various domains like social networks, recommendation systems, bioinformatics, and more.

Graph processing, especially the processing of large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. However, it remains a great challenge to process such large-scale graphs on memory limited accelerators. This book tries to introduce some recent techniques to unleash the power of parallel computing by using recent hardware accelerators like GPU/FPGA.

This comprehensive book covers several key features essential for maximizing efficiency and performance in GPU-based computing. Readers will learn to master GPU memory utilization techniques to enhance algorithmic speed and implement graph traversal and processing algorithms using high-performance CUDA programming. The guide also explores the potential of parallel computing for graph analytics, providing optimization strategies for diverse graph structures and algorithmic complexities. To ensure practical understanding, the book includes real-world case studies and practical examples for hands-on learning.

Whether you're a researcher, data scientist, or enthusiast in GPU computing, this book is your gateway to unlocking the full potential of graph processing in the era of parallel computing. Elevate your expertise and revolutionize your approach to graph analysis with this essential resource.

Focuses on strategies to maximize performance by leveraging the parallel processing power of recent accelerators Covers cutting-edge techniques of hardware-accelerated graph processing Provides real-world applications in various fields such as social network analysis, machine learning, and bioinformatics

Autorentext

Zhigao Zheng is with Wuhan University. He is an executive committee member of the Technical Committee on Distributed Computing & Systems and Embedded Systems of China Computer Federation (CCF), and a Standing Committee Member of the Large Model and Decision Intelligence Committee of the Chinese Institute of Command and Control (CICC). His current research interests focus on cloud computing, big data processing, and AI systems. He has won some awards, such as the Second prize of CICC Scientific and Technological Progress, Youth Science and Technology Award from the Collaborative Computing Committee of the CCF, Rising Star of ACM-Wuhan, and IEEE TCSC Outstanding Ph.D. Dissertation Award. He published more than 30 peer-reviewed publications in the top journals and conferences of parallel & distributed computing (such as the IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, IEEE Transactions on Knowledge and Data Engineering, IEEE INFOCOM, and IEEE ICDE). He is the PC member of several TOP conferences, such as TheWebConf (formally WWW), NeurIPS, AAAI, and the vice PC chair of CPSCom 2023. He is also the editorial board member of several high-quality journals, such as the IEEE Transactions on Consumer Electronics, Mobile Networks and Applications. He joins research projects from various governmental and industrial organizations, such as the National Science Foundation of China (NSFC), the Ministry of Science and Technology, and the Ministry of Education. He is a member of ACM, IEEE, and CCF.

Inhalt

"Chapter I: Recent Accelerators".- "Chapter II: Graph Traversal Algorithms on GPU".- "Chapter III: Graph Analysis Algorithms on GPU".- "Chapter IV: Graph Mining Algorithms on GPU".- "Chapter V: Performance Analysis of Different Accelerators".- "Chapter VI: Applications and Related Topics".