191 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Parameterized Algorithms for String Matching to DAGs: Funnels and Beyond
The problem of String Matching to Labeled Graphs (SMLG) asks to find all the paths in a labeled graph G = (V, E) whose spellings match that of an input string S ? ?^m. SMLG can be solved in quadratic O(m|E|) time [Amir et al., JALG 2000], which was proven to be optimal by a recent lower bound conditioned on SETH [Equi et al., ICALP 2019]. The lower bound states that no strongly subquadratic time algorithm exists, even if restricted to directed acyclic graphs (DAGs).
In this work we present the first parameterized algorithms for SMLG on DAGs. Our parameters capture the topological structure of G. All our results are derived from a generalization of the Knuth-Morris-Pratt algorithm [Park and Kim, CPM 1995] optimized to work in time proportional to the number of prefix-incomparable matches.
To obtain the parameterization in the topological structure of G, we first study a special class of DAGs called funnels [Millani et al., JCO 2020] and generalize them to k-funnels and the class ST_k. We present several novel characterizations and algorithmic contributions on both funnels and their generalizations
Patrolling Grids with a Bit of Memory
We study the following problem in elementary robotics: can a mobile agent
with bits of memory, which is able to sense only locations at Manhattan
distance or less from itself, patrol a -dimensional grid graph? We show
that it is impossible to patrol some grid graphs with bits of memory,
regardless of , and give an exact characterization of those grid graphs that
can be patrolled with bits of memory and visibility range . On the other
hand, we show that, surprisingly, an algorithm exists using bit of memory
and that patrols any -dimensional grid graph
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
LIPIcs, Volume 258, SoCG 2023, Complete Volume
LIPIcs, Volume 258, SoCG 2023, Complete Volum
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Computational Methods in Multi-Messenger Astrophysics using Gravitational Waves and High Energy Neutrinos
This dissertation seeks to describe advancements made in computational methods for multi-messenger astrophysics (MMA) using gravitational waves GW and neutrinos during Advanced LIGO (aLIGO)’s first through third observing runs (O1-O3) and, looking forward, to describe novel computational techniques suited to the challenges of both the burgeoning MMA field and high-performance computing as a whole.
The first two chapters provide an overview of MMA as it pertains to gravitational wave/high energy neutrino (GWHEN) searches, including a summary of expected astrophysical sources as well as GW, neutrino, and gamma-ray detectors used in their detection. These are followed in the third chapter by an in-depth discussion of LIGO’s timing system, particularly the diagnostic subsystem, describing both its role in MMA searches and the author’s contributions to the system itself.
The fourth chapter provides a detailed description of the Low-Latency Algorithm for Multi-messenger Astrophysics (LLAMA), the GWHEN pipeline developed by the author and used in O2 and O3. Relevant past multi-messenger searches are described first, followed by the O2 and O3 analysis methods, the pipeline’s performance, scientific results, and finally, an in-depth account of the library’s structure and functionality. In particular, the author’s high-performance multi-order coordinates (MOC) HEALPix image analysis library, HPMOC, is described. HPMOC increases performance of HEALPix image manipulations by several orders of magnitude vs. naive single-resolution approaches while presenting a simple high-level interface and should prove useful for diverse future MMA searches. The performance improvements it provides for LLAMA are also covered.
The final chapter of this dissertation builds on the approaches taken in developing HPMOC, presenting several novel methods for efficiently storing and analyzing large data sets, with applications to MMA and other data-intensive fields. A family of depth-first multi-resolution ordering of HEALPix images — DEPTH9, DEPTH19, and DEPTH40 — is defined, along with algorithms and use cases where it can improve on current approaches, including high-speed streaming calculations suitable for serverless compute or FPGAs.
For performance-constrained analyses on HEALPix data (e.g. image analysis in multi-messenger search pipelines) using SIMD processors, breadth-first data structures can provide short-circuiting calculations in a data-parallel way on compressed data; a simple compression method is described with application to further improving LLAMA performance.
A new storage scheme and associated algorithms for efficiently compressing and contracting tensors of varying sparsity is presented; these demuxed tensors (D-Tensors) have equivalent asymptotic time and space complexity to optimal representations of both dense and sparse matrices, and could be used as a universal drop-in replacement to reduce code complexity and developer effort while improving performance of existing non-optimized numerical code. Finally, the big bucket hash table (B-Table), a novel type of hash table making guarantees on data layout (vs. load factor), is described, along with optimizations it allows for (like hardware acceleration, online rebuilds, and hard realtime applications) that are not possible with existing hash table approaches. These innovations are presented in the hope that some will prove useful for improving future MMA searches and other data-intensive applications
Computer Aided Verification
This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
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