Arbitrarily accurate approximation scheme for large-scale RFID cardinality estimation

Author(s):  
Wei Gong ◽  
Kebin Liu ◽  
Xin Miao ◽  
Haoxiang Liu
2016 ◽  
Vol 194 ◽  
pp. 107-116 ◽  
Author(s):  
Jingsong Shan ◽  
Jianxin Luo ◽  
Guiqiang Ni ◽  
Zhaofeng Wu ◽  
Weiwei Duan

2020 ◽  
Vol 23 (1-4) ◽  
Author(s):  
Ruth Schöbel ◽  
Robert Speck

AbstractTo extend prevailing scaling limits when solving time-dependent partial differential equations, the parallel full approximation scheme in space and time (PFASST) has been shown to be a promising parallel-in-time integrator. Similar to space–time multigrid, PFASST is able to compute multiple time-steps simultaneously and is therefore in particular suitable for large-scale applications on high performance computing systems. In this work we couple PFASST with a parallel spectral deferred correction (SDC) method, forming an unprecedented doubly time-parallel integrator. While PFASST provides global, large-scale “parallelization across the step”, the inner parallel SDC method allows integrating each individual time-step “parallel across the method” using a diagonalized local Quasi-Newton solver. This new method, which we call “PFASST with Enhanced concuRrency” (PFASST-ER), therefore exposes even more temporal concurrency. For two challenging nonlinear reaction-diffusion problems, we show that PFASST-ER works more efficiently than the classical variants of PFASST and can use more processors than time-steps.


2017 ◽  
Vol 66 ◽  
pp. 52-63 ◽  
Author(s):  
Qing Cao ◽  
Yunhe Feng ◽  
Zheng Lu ◽  
Hairong Qi ◽  
Leon M. Tolbert ◽  
...  

2017 ◽  
Vol 25 (3) ◽  
pp. 1347-1358 ◽  
Author(s):  
Wei Gong ◽  
Jiangchuan Liu ◽  
Kebin Liu ◽  
Yunhao Liu

2017 ◽  
Vol 83 ◽  
pp. 101-110 ◽  
Author(s):  
Jonghoon Park ◽  
Cheoleun Moon ◽  
Ikjun Yeom ◽  
Yusung Kim

2016 ◽  
Vol 14 (01) ◽  
pp. 1640002 ◽  
Author(s):  
Shuzhi Yu ◽  
Fanchang Hao ◽  
Hon Wai Leong

We consider the problem of sorting signed permutations by reversals, transpositions, transreversals, and block-interchanges. The problem arises in the study of species evolution via large-scale genome rearrangement operations. Recently, Hao et al. gave a 2-approximation scheme called genome sorting by bridges (GSB) for solving this problem. Their result extended and unified the results of (i) He and Chen — a 2-approximation algorithm allowing reversals, transpositions, and block-interchanges (by also allowing transversals) and (ii) Hartman and Sharan — a 1.5-approximation algorithm allowing reversals, transpositions, and transversals (by also allowing block-interchanges). The GSB result is based on introduction of three bridge structures in the breakpoint graph, the L-bridge, T-bridge, and X-bridge that models goodreversal, transposition/transreversal, and block-interchange, respectively. However, the paper by Hao et al. focused on proving the 2-approximation GSB scheme and only mention a straightforward [Formula: see text] algorithm. In this paper, we give an [Formula: see text] algorithm for implementing the GSB scheme. The key idea behind our faster GSB algorithm is to represent cycles in the breakpoint graph by their canonical sequences, which greatly simplifies the search for these bridge structures. We also give some comparison results (running time and computed distances) against the original GSB implementation.


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