2,620 research outputs found
Lock-free Concurrent Data Structures
Concurrent data structures are the data sharing side of parallel programming.
Data structures give the means to the program to store data, but also provide
operations to the program to access and manipulate these data. These operations
are implemented through algorithms that have to be efficient. In the sequential
setting, data structures are crucially important for the performance of the
respective computation. In the parallel programming setting, their importance
becomes more crucial because of the increased use of data and resource sharing
for utilizing parallelism.
The first and main goal of this chapter is to provide a sufficient background
and intuition to help the interested reader to navigate in the complex research
area of lock-free data structures. The second goal is to offer the programmer
familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing
Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and
Distributed Computin
Judicial Errors: Fake Imaging and the Modern Law of Evidence, 21 UIC Rev. Intell. Prop. L. 82 (2022)
Self-stabilizing tree algorithms
Designers of distributed algorithms have to contend with the problem of making the algorithms tolerant to several forms of coordination loss, primarily faulty initialization. The processes in a distributed system do not share a global memory and can only get a partial view of the global state. Transient failures in one part of the system may go unnoticed in other parts and thus cause the system to go into an illegal state. If the system were self-stabilizing, however, it is guaranteed that it will return to a legal state after a finite number of state transitions. This thesis presents and proves self-stabilizing algorithms for calculating tree metrics and for achieving mutual exclusion on a tree structured distributed system
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