Task Scheduling for Multi-core and Parallel Architectures (eBook)

This book presents task-scheduling techniques for emerging complex parallel architectures including heterogeneous multi-core architectures, warehouse-scale datacenters, and distributed big data processing systems. The demand for high computational capacity has led to the growing popularity of multicore processors, which have become the mainstream in both the research and real-world settings. Yet to date, there is no book exploring the current task-scheduling techniques for the emerging complex parallel architectures.

Addressing this gap, the book discusses state-of-the-art task-scheduling techniques that are optimized for different architectures, and which can be directly applied in real parallel systems. Further, the book provides an overview of the latest advances in task-scheduling policies in parallel architectures, and will help readers understand and overcome current and emerging issues in this field.

Quan Chen is currently an assistant professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China. Before joining the SJTU, he pursued his post-doctoral research at the University of Michigan's Department of Computer Science, Ann Arbor, USA from 2014 to 2016. He received his MS degree in 2009 and his PhD degree in 2014, both from the SJTU. During his PhD, he was a research associate at the Department of Computer Science of Columbia University, USA from 2013 to 2014. From 2010 to 2011, he was a visiting scholar at the Department of Computer Science, University of Otago, New Zealand. His research interests include high-performance computing, task scheduling for various architectures, and resource management in datacenters, runtime systems and operating systems. His dissertation was honored with the Shanghai Excellent Doctoral Dissertation Award and the China Computer Federation (CCF) Excellent Doctoral Dissertation Award.

Minyi Guo is a Zhiyuan Chair Professor and head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), Shanghai, China. He is also the director of the SJTU's Embedded and Pervasive Computing Center. He received his BS and ME degrees in Computer Science from Nanjing University, China in 1982 and 1986, respectively. From 1986 to 1994, he served as an assistant professor at the Department of Computer Science, Nanjing University. He received his PhD degree in Information Science from the University of Tsukuba, Japan in 1998. His research interests include parallel and distributed processing, parallelizing compilers, cloud computing, pervasive computing, software engineering, embedded systems, green computing, and wireless sensor networks. He is an associate editor for IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Parallel and Distributed Computing (JPDC), and Journal of Computer Science and Technology (JCST).

He has published numerous articles in prominent journals, and has authored books with Springer. Further, he has led many research projects including Natural Science Foundation of China (NSFC) projects, 863 projects and 973 projects.  

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This book presents task-scheduling techniques for emerging complex parallel architectures including heterogeneous multi-core architectures, warehouse-scale datacenters, and distributed big data processing systems. The demand for high computational capacity has led to the growing popularity of multicore processors, which have become the mainstream in both the research and real-world settings. Yet to date, there is no book exploring the current task-scheduling techniques for the emerging complex parallel architectures. Addressing this gap, the book discusses state-of-the-art task-scheduling techniques that are optimized for different architectures, and which can be directly applied in real parallel systems. Further, the book provides an overview of the latest advances in task-scheduling policies in parallel architectures, and will help readers understand and overcome current and emerging issues in this field.