Tetris–Hashing or optimal table compression

Galli, Nicola; Seybold, Bernhard; Simon, Klaus

doi:10.1016/s0166-218x(00)00302-4

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, one will quickly realize that the Tetris that they speak of is not the game so beloved by humanity, but rather "Tetris is an Asynchronous Byzantine Fault Tolerance consensus algorithm designed for next generation high-throughput permission and permissionless blockchain." In continuing though the literature, one quickly realizes that the name "Tetris" has been appropriated mercilessly to entitle and describe things that are not directly about the famed game, but are either meant to invoke the idea of putting pieces together or are just an acronym that the authors found cute, no doubt in the hopes that a small fraction of the shine of the real and true Tetris might befall upon their work [80,99,100,92,95,51,159,148,46,126,55,136,20,126,96,152,153,4,83,58,34,91,97,52,161,87,157,27,160]. Perhaps the most useful of the Tetris-inspired research has been the creation of a floor cleaning robot which is hinged and can change shape to various Tetris pieces [117,82,118].…”

Section: Related Workmentioning

confidence: 99%

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Dallant¹,

Iacono²

2022

Preprint

View full text Add to dashboard Cite

Consider a variant of Tetris played on a board of width w and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of O(n) rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [Pătraşcu, 2010] that on a board of width w = Θ(n), if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time O(n 1/2− ) simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in O(n 1/2 log 3/2 n) time on boards of width n O(1) , matching the lower bound up to a n o(1) factor.

show abstract

Section: Related Workmentioning

confidence: 99%

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Dallant¹,

Iacono²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Among these algorithms, the AC algorithm based on automata is very robust in practice, but the large memory usage of automata limits its application for large collections of patterns. To achieve a compact representation, a large number of solutions have been proposed by several researchers, including Tarjan and Yao , Fredman et al , Galli et al , Dencker et al , Kiraz , Aoe , and Mizobuchi et al . .…”

Section: Related Workmentioning

confidence: 99%

An efficient parallel algorithm for exact multi‐pattern matching

Zhang

Tian

et al. 2014

Security Comm Networks

View full text Add to dashboard Cite

This paper presents a parallel algorithm Parallel Extended Bloom Filter (PEBF) for exact multi‐pattern matching based on Bloom filter. To improve the throughput and parallelism of the algorithm, we divided the pattern set into N subsets where the length of patterns is the same, and different subsets would not intersect each other. We construct an EBF for each subset and use N threads to simultaneously process the subsets in parallel. We implement our solution on the graphics processing unit, called G‐PEBF. Experimental results demonstrate that PEBF performs better than the Wu–Manber (WM) algorithm in terms of time and space. And G‐PEBF outperforms the G‐WM (WM algorithm implemented on graphics processing unit). The speedup of G‐PEBF is up to 60 times at peak performance and almost 10 times at worst performance to the PEBF algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract