3,758 research outputs found
Parallel Longest Common SubSequence Analysis In Chapel
One of the most critical problems in the field of string algorithms is the
longest common subsequence problem (LCS). The problem is NP-hard for an
arbitrary number of strings but can be solved in polynomial time for a fixed
number of strings. In this paper, we select a typical parallel LCS algorithm
and integrate it into our large-scale string analysis algorithm library to
support different types of large string analysis. Specifically, we take
advantage of the high-level parallel language, Chapel, to integrate Lu and
Liu's parallel LCS algorithm into Arkouda, an open-source framework. Through
Arkouda, data scientists can easily handle large string analytics on the
back-end high-performance computing resources from the front-end Python
interface. The Chapel-enabled parallel LCS algorithm can identify the longest
common subsequences of two strings, and experimental results are given to show
how the number of parallel resources and the length of input strings can affect
the algorithm's performance.Comment: The 27th Annual IEEE High Performance Extreme Computing Conference
(HPEC), Virtual, September 25-29, 202
Fast and Deterministic Constant Factor Approximation Algorithms for LCS Imply New Circuit Lower Bounds
The Longest Common Subsequence (LCS) is one of the most basic similarity measures and it captures important applications in bioinformatics and text analysis. Following the SETH-based nearly-quadratic time lower bounds for LCS from recent years, it is a major open problem to understand the complexity of approximate LCS.
In the last ITCS [AB17] drew an interesting connection between this problem and the area of circuit complexity:
they proved that approximation algorithms for LCS in deterministic truly-subquadratic time imply new circuit lower bounds (E^NP does not have non-uniform linear-size Valiant Series Parallel circuits).
In this work, we strengthen this connection between approximate LCS and circuit complexity by applying the Distributed PCP framework of [ARW17].
We obtain a reduction that holds against much larger approximation factors (super-constant versus 1+o(1)), yields a lower bound for a larger class of circuits (linear-size NC^1), and is also easier to analyze
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