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pro vyhledávání: '"Tsigas, P."'
Autor:
von Geijer, Kåre, Tsigas, Philippas
The sequential semantics of many concurrent data structures, such as stacks and queues, inevitably lead to memory contention in parallel environments, thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing
Externí odkaz:
http://arxiv.org/abs/2403.13644
Autor:
Argyrios Periferakis, Georgios Tsigas, Aristodemos-Theodoros Periferakis, Carla Mihaela Tone, Daria Alexandra Hemes, Konstantinos Periferakis, Lamprini Troumpata, Ioana Anca Badarau, Cristian Scheau, Ana Caruntu, Ilinca Savulescu-Fiedler, Constantin Caruntu, Andreea-Elena Scheau
Publikováno v:
Current Issues in Molecular Biology, Vol 46, Iss 9, Pp 9721-9759 (2024)
Somatostatin is a peptide that plays a variety of roles such as neurotransmitter and endocrine regulator; its actions as a cell regulator in various tissues of the human body are represented mainly by inhibitory effects, and it shows potent activity
Externí odkaz:
https://doaj.org/article/f2a49fd9d0a1477e88d88a284a9fe12f
Autor:
Gulisano, Vincenzo, Najdataei, Hannaneh, Nikolakopoulos, Yiannis, Papadopoulos, Alessandro V., Papatriantafilou, Marina, Tsigas, Philippas
Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale stream processing applications. Given an application, SN parallelism
Externí odkaz:
http://arxiv.org/abs/2111.13058
Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, d
Externí odkaz:
http://arxiv.org/abs/2102.09032
Autor:
Najdataei, Hannaneh, Gulisano, Vincenzo, Papadopoulos, Alessandro V., Walulya, Ivan, Papatriantafilou, Marina, Tsigas, Philippas
Streaming analysis is widely used in cloud as well as edge infrastructures. In these contexts, fine-grained application performance can be based on accurate modeling of streaming operators. This is especially beneficial for computationally expensive
Externí odkaz:
http://arxiv.org/abs/2005.04935
Autor:
Natalie A. Cameron, Sadiya S. Khan, Alina N. Brewer, Eleni Z. Tsigas, Roberta B. Ness, James M. Roberts
Publikováno v:
American Heart Journal Plus, Vol 34, Iss , Pp 100319- (2023)
Introduction: Recruiting women with a family history (FH) of hypertensive disorders of pregnancy (HDP) to participate in research before pregnancy could offer insight into genetic and lifestyle factors that incur higher risk of cardiovascular disease
Externí odkaz:
https://doaj.org/article/4a91da2fc9c54279a474861e5106366c
Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several Machine Learning and Data Analytics methods. Recently there have been
Externí odkaz:
http://arxiv.org/abs/1911.03444
There has been a significant amount of work in the literature proposing semantic relaxation of concurrent data structures for improving scalability and performance. By relaxing the semantics of a data structure, a bigger design space, that allows wea
Externí odkaz:
http://arxiv.org/abs/1906.07105
Akademický článek
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This paper considers the modelling and the analysis of the performance of lock-free concurrent search data structures. Our analysis considers such lock-free data structures that are utilized through a sequence of operations which are generated with a
Externí odkaz:
http://arxiv.org/abs/1805.04794