48 research outputs found
Parallel and Distributed Data Series Processing on Modern and Emerging Hardware
This paper summarizes state-of-the-art results on data series processing with
the emphasis on parallel and distributed data series indexes that exploit the
computational power of modern computing platforms. The paper comprises a
summary of the tutorial the author delivered at the 15th International
Conference on Management of Digital EcoSystems (MEDES'23).Comment: This paper will appear in the Proceedings of the 15th International
Conference on Management of Digital EcoSystems (MEDES'23
Brief Announcement: Persistent Software Combining
We study the performance power of software combining in designing recoverable algorithms and data structures. We present two recoverable synchronization protocols, one blocking and another wait-free, which illustrate how to use software combining to achieve both low persistence and synchronization cost. Our experiments show that these protocols outperform by far state-of-the-art recoverable universal constructions and transactional memory systems. We built recoverable queues and stacks, based on these protocols, that exhibit much better performance than previous such implementations
FreSh: A Lock-Free Data Series Index
We present FreSh, a lock-free data series index that exhibits good
performance (while being robust). FreSh is based on Refresh, which is a generic
approach we have developed for supporting lock-freedom in an efficient way on
top of any localityaware data series index. We believe Refresh is of
independent interest and can be used to get well-performed lock-free versions
of other locality-aware blocking data structures. For developing FreSh, we
first studied in depth the design decisions of current state-of-the-art data
series indexes, and the principles governing their performance. This led to a
theoretical framework, which enables the development and analysis of data
series indexes in a modular way. The framework allowed us to apply Refresh,
repeatedly, to get lock-free versions of the different phases of a family of
data series indexes. Experiments with several synthetic and real datasets
illustrate that FreSh achieves performance that is as good as that of the
state-of-the-art blocking in-memory data series index. This shows that the
helping mechanisms of FreSh are light-weight, respecting certain principles
that are crucial for performance in locality-aware data structures.This paper
was published in SRDS 2023.Comment: 12 pages, 18 figures, Conference: Symposium on Reliable Distributed
Systems (SRDS 2023