Ivan Walulya, Computer Science and Engineering
On Design and Applications of Practical Concurrent Data Structures
In recent years, multicore systems have become ubiquitous; processors in ultra-low power embedded systems to supercomputers contain multiple cores. This proliferation of multicore processors is having an enormous impact on how we design and implement software systems. Shared-memory multicore processors are systems on which multiple computation threads can execute concurrently with access to shared system resources. However, the access to shared resources needs to be synchronized; which is generally the cause of significant difficulty with utilizing multicore systems.
In order to efficiently utilize these multicore processors, we need to design and implement concurrent programming abstractions that programmers at all levels of expertise can trivially use for general-purpose applications development. A common abstraction for synchronized access to shared data is a concurrent data structure. Concurrent data structures are challenging to design and implement due to the requirement to be correct, efficient and practical under various application constraints.
In this thesis, we propose new techniques for designing efficient concurrent data structures and improvements to existing implementations. We explore design approaches that are easy to implement without concern for the programming language or deployment platform. Additionally, we explore how to utilize concurrent data structures in complex applications, especially those with stringent throughput and latency demands such as data stream processing.
Ivan Walulya belongs to the Networks and Systems division of Computer Science and Engineering.
Assoc. prof. Danny Hendler, Ben-Gurion University, Israel.
Prof. Lasse Natvig, Norwegian University of Science and Technology, Norway.
Dr. Emanuelle Anceaume, IRISA, France.
Prof. Håkan Grahn, Blekinge Institute of Technology, Sweden.
ED, lecture hall,
27 November, 2018, 10:00
27 November, 2018, 11:00