Invited Lectures.- Tableau Algorithms for Description Logics.- Modality and Databases.- Local Symmetries in Propositional Logic.- Comparison.- Design and Results of TANCS-2000 Non-classical (Modal) Systems Comparison.- Consistency Testing: The RACE Experience.- Benchmark Analysis with FaCT.- MSPASS: Modal Reasoning by Translation and First-Order Resolution.- TANCS-2000 Results for DLP.- Evaluating *SAT on TANCS 2000 Benchmarks.- Research Papers.- A Labelled Tableau Calculus for Nonmonotonic (Cumulative) Consequence Relations.- A Tableau System for Godel-Dummett Logic Based on a Hypersequent Calculus.- An Analytic Calculus for Quantified Propositional Godel Logic.- A Tableau Method for Inconsistency-Adaptive Logics.- A Tableau Calculus for Integrating First-Order and Elementary Set Theory Reasoning.- Hypertableau and Path-Hypertableau Calculi for some Families of Intermediate Logics.- Variants of First-Order Modal Logics.- Complexity of Simple Dependent Bimodal Logics.- Properties of Embeddings from Int to S4.- Term-Modal Logics.- A Subset-Matching Size-Bounded Cache for Satisfiability in Modal Logics.- Dual Intuitionistic Logic Revisited.- Model Sets in a Nonconstructive Logic of Partial Terms with Definite Descriptions.- Search Space Compression in Connection Tableau Calculi Using Disjunctive Constraints.- Matrix-Based Inductive Theorem Proving.- Monotonic Preorders for Free Variable Tableaux.- The Mosaic Method for Temporal Logics.- Sequent-Like Tableau Systems with the Analytic Superformula Property for the Modal Logics KB, KDB, K5, KD5.- A Tableau Calculus for Equilibrium Entailment.- Towards Tableau-Based Decision Procedures for Non-Well-Founded Fragments of Set Theory.- Tableau Calculus for Only Knowing and Knowing At Most.- A Tableau-Like Representation Framework for Efficient Proof Reconstruction.- The Semantic Tableaux Version of the Second Incompleteness Theorem Extends Almost to Robinson's Arithmetic Q.- System Descriptions.- Redundancy-Free Lemmatization in the Automated Model-Elimination Theorem Prover AI-SETHEO.- E-SETHEO: An Automated3 Theorem Prover.

Automated Reasoning with Analytic Tableaux and Related Methods: International Conference, TABLEAUX 2000 St Andrews, Scotland, UK, July 3-7, 2000 Proceedings

2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

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A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

Real-time simulation of contact and cutting of heterogeneous soft-tissues

Irregular algorithms are algorithms with complex main data structures such as directed and undirected graphs, trees, etc. A useful abstraction for many irregular algorithms is its operator formulation in which the algorithm is viewed as the iterated application of an operator to certain nodes, called active nodes, in the graph. Each operator application, called an activity, usually touches only a small part of the overall graph, so nonoverlapping activities can be performed in parallel. In topology-driven implementations, all nodes are assumed to be active so the operator is applied everywhere in the graph even if there is no work to do at some nodes. In contrast, in data-driven implementations the operator is applied only to nodes at which there might be work to do. Multicore implementations of irregular algorithms are usually data-driven because current multicores only support small numbers of threads and work-efficiency is important. Conversely, many irregular GPU implementations use a topology-driven approach because work inefficiency can be counterbalanced by the large number of GPU threads. In this paper, we study data-driven and topology-driven implementations of six important graph algorithms on GPUs. Our goal is to understand the tradeoffs between these implementations and how to optimize them. We find that data-driven versions are generally faster and scale better despite the cost of maintaining a worklist. However, topology-driven versions can be superior when certain algorithmic properties are exploited to optimize the implementation. These results led us to devise hybrid approaches that combine the two techniques and outperform both of them.

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Data-Driven Versus Topology-driven Irregular Computations on GPUs

The Single-Source Shortest Path (SSSP) problem arises in many different fields. In this paper we present a GPU-based version of the Crauser et al. SSSP algorithm. Our work significantly speeds up the computation of the SSSP, not only with respect to the CPU-based version, but also to other state-of-the-art GPU implementation based on Dijkstra, due to Martin et al. Both GPU implementations have been evaluated using the last Nvidia architecture (Kepler). Our experimental results show that the new GPU-Crauser algorithm leads to speed-ups from 13× to 220× with respect to the CPU version and a performance gain of up to 17% with respect the GPU-Marten algorithm.

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A new GPU-based approach to the Shortest Path problem

We present several algorithms that solve the single-source shortest-path problem using CUDA. We have run them on a database, composed of hundreds of large graphs represented by adjacency lists and adjacency matrices, achieving high speedups regarding a CPU implementation based on Fibonacci heaps. Concerning correctness, we outline why our solutions work, and show that a previous approach [10] is incorrect.

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CUDA Solutions for the SSSP Problem

Many general-purpose applications exploit Graphics Processing Units (GPUs) by executing a set of well-known dataparallel primitives. Those primitives are usually invoked from the host many times, so their throughput has a great impact on the performance of the overall system. Thus, the study of novel algorithmic strategies to optimize their implementation on current devices is an interesting topic to the GPU community. In this paper we focus on optimizing the reduction primitive, which merely reduces a data sequence into a single value using a binary associative operator. Although tree-based and sequential-based algorithms have been already implemented on GPUs, a comparison of both algorithm performance had not been carried out yet. Thus, our first contribution is to present an experimental study of state-of-the-art reduction algorithms on CUDA. Next we introduce two algorithmic optimizations that are integrated into the fastest solution (a sequential-based algorithm), improving its throughput even more. Finally, we replicate this methodology to the segmented version of the primitive, which applies when the input is composed of several independent segments. In this case, it is not clear which algorithm exhibits the best performance, since throughput deeply depends on the distribution of segments along the input. According to our results, tree-based algorithms run faster for small segments, while sequential methods are better for medium and large ones.

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Algorithmic strategies for optimizing the parallel reduction primitive in CUDA

In this paper, we present a real-time ray caster implemented on GPU using CUDA. It uses a BVH augmented with ropes that is traversed with ray packets to speed up performance. We present two algorithms making use of ray packets, packet-warp and packet-block, which set the packet size to a warp and a block, respectively. We also analyze the influence of the packet size and packet shape by testing several configurations. Finally, we compare the time results we have obtained to previous related papers over a batch of usual scenes.

Ray casting using a roped BVH with CUDA

In this paper we present a three valued many sorted logic for dealing with preorders, incorporating subsort relations into the syntax of the language, and where formulas taking the third boolean value as interpretation contain a term or a predicate which is not well-sorted w.r.t. the signature. For this logic a ground tableau-based deduction method and a free variable extension version are proposed, proving their completeness.

Reasoning with Preorders and Dynamic Sorts Using Free Variable Tableaux

Algorithm design is one of the more neglected aspects in programming introduction courses. On the contrary, schemas focus on solution construction, since they gather common characteristics of algorithms, so they can be considered as algorithm cognitive units. In this paper, we go beyond the benefits of teaching schemas and we present a tool that incorporates their use. It automatically generates code from the application of schemas, allowing its integration into the class as a useful educational tool.

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Antonio Gavilanes

Papers

CUDA Solutions for the SSSP Problem

Algorithmic strategies for optimizing the parallel reduction primitive in CUDA

Ray casting using a roped BVH with CUDA

Reasoning with Preorders and Dynamic Sorts Using Free Variable Tableaux

A Tool for Automatic Code Generation from Schemas