101 research outputs found
09491 Abstracts Collection -- Graph Search Engineering
From the 29th November to the 4th December 2009, the Dagstuhl Seminar
09491 ``Graph Search Engineering \u27\u27 was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
On the Scalability of the GPUexplore Explicit-State Model Checker
The use of graphics processors (GPUs) is a promising approach to speed up
model checking to such an extent that it becomes feasible to instantly verify
software systems during development. GPUexplore is an explicit-state model
checker that runs all its computations on the GPU. Over the years it has been
extended with various techniques, and the possibilities to further improve its
performance have been continuously investigated. In this paper, we discuss how
the hash table of the tool works, which is at the heart of its functionality.
We propose an alteration of the hash table that in isolated experiments seems
promising, and analyse its effect when integrated in the tool. Furthermore, we
investigate the current scalability of GPUexplore, by experimenting both with
input models of varying sizes and running the tool on one of the latest GPUs of
NVIDIA.Comment: In Proceedings GaM 2017, arXiv:1712.0834
GPU Accelerated counterexample generation in LTL model checking
Strongly Connected Component (SCC) based searching is one of the most popular LTL model checking algorithms. When the SCCs are huge, the counterexample generation process can be time-consuming, especially when dealing with fairness assumptions. In this work, we propose a GPU accelerated counterexample generation algorithm, which improves the performance by parallelizing the Breadth First Search (BFS) used in the counterexample generation. BFS work is irregular, which means it is hard to allocate resources and may suffer from imbalanced load. We make use of the features of latest CUDA Compute Architecture-NVIDIA Kepler GK110 to achieve the dynamic parallelism and memory hierarchy so as to handle the irregular searching pattern in BFS.We build dynamic queue management, task scheduler and path recording such that the counterexample generation process can be completely finished by GPU without involving CPU. We have implemented the proposed approach in PAT model checker. Our experiments show that our approach is effective and scalable. ?Springer International Publishing Switzerland 2014.EI0413-429882
A GNN Based Approach to LTL Model Checking
Model Checking is widely applied in verifying complicated and especially
concurrent systems. Despite of its popularity, model checking suffers from the
state space explosion problem that restricts it from being applied to certain
systems, or specifications. Many works have been proposed in the past to
address the state space explosion problem, and they have achieved some success,
but the inherent complexity still remains an obstacle for purely symbolic
approaches. In this paper, we propose a Graph Neural Network (GNN) based
approach for model checking, where the model is expressed using a B{\"u}chi
automaton and the property to be verified is expressed using Linear Temporal
Logic (LTL). We express the model as a GNN, and propose a novel node embedding
framework that encodes the LTL property and characteristics of the model. We
reduce the LTL model checking problem to a graph classification problem, where
there are two classes, 1 (if the model satisfies the specification) and 0 (if
the model does not satisfy the specification). The experimental results show
that our framework is up to 17 times faster than state-of-the-art tools. Our
approach is particularly useful when dealing with very large LTL formulae and
small to moderate sized models
Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems
Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft
Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS
PRISM-PSY:Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems
In this paper we present PRISM-PSY, a novel tool that performs
precise GPU-accelerated parameter synthesis for continuous-time
Markov chains and time-bounded temporal logic specifications. We redesign,
in terms of matrix-vector operations, the recently formulated algorithms
for precise parameter synthesis in order to enable effective dataparallel
processing, which results in significant acceleration on many-core
architectures. High hardware utilisation, essential for performance and
scalability, is achieved by state space and parameter space parallelisation:
the former leverages a compact sparse-matrix representation, and the latter
is based on an iterative decomposition of the parameter space. Our
experiments on several biological and engineering case studies demonstrate
an overall speedup of up to 31-fold on a single GPU compared to
the sequential implementation
Model Checking Temporal Logic Formulas Using Sticker Automata
As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method
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