103,847 research outputs found
Unfolding-based Partial Order Reduction
Partial order reduction (POR) and net unfoldings are two alternative methods
to tackle state-space explosion caused by concurrency. In this paper, we
propose the combination of both approaches in an effort to combine their
strengths. We first define, for an abstract execution model, unfolding
semantics parameterized over an arbitrary independence relation. Based on it,
our main contribution is a novel stateless POR algorithm that explores at most
one execution per Mazurkiewicz trace, and in general, can explore exponentially
fewer, thus achieving a form of super-optimality. Furthermore, our
unfolding-based POR copes with non-terminating executions and incorporates
state-caching. Over benchmarks with busy-waits, among others, our experiments
show a dramatic reduction in the number of executions when compared to a
state-of-the-art DPOR.Comment: Long version of a paper with the same title appeared on the
proceedings of CONCUR 201
Recommended from our members
Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
Symbolic Partial-Order Execution for Testing Multi-Threaded Programs
We describe a technique for systematic testing of multi-threaded programs. We
combine Quasi-Optimal Partial-Order Reduction, a state-of-the-art technique
that tackles path explosion due to interleaving non-determinism, with symbolic
execution to handle data non-determinism. Our technique iteratively and
exhaustively finds all executions of the program. It represents program
executions using partial orders and finds the next execution using an
underlying unfolding semantics. We avoid the exploration of redundant program
traces using cutoff events. We implemented our technique as an extension of
KLEE and evaluated it on a set of large multi-threaded C programs. Our
experiments found several previously undiscovered bugs and undefined behaviors
in memcached and GNU sort, showing that the new method is capable of finding
bugs in industrial-size benchmarks.Comment: Extended version of a paper presented at CAV'2
Symmetry reduction and heuristic search for error detection in model checking
The state explosion problem is the main limitation of model checking. Symmetries in the system being verified can be exploited in order to avoid this problem by defining an equivalence (symmetry) relation on the states of the system, which induces a semantically equivalent quotient system of smaller size. On the other hand, heuristic search algorithms can be applied to improve the bug finding capabilities of model checking. Such algorithms use
heuristic functions to guide the exploration. Bestfirst
is used for accelerating the search, while A* guarantees optimal error trails if combined with admissible estimates. We analyze some aspects of combining both approaches, concentrating on the problem of finding the optimal path to the equivalence class of a given error state. Experimental
results evaluate our approach
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
- …