199,546 research outputs found
Memory consistency models using constraints
Memory consistency models (MCMs) are at the heart of concurrent programming. They represent the behaviour of concurrent programs at the chip level. To test these models small program snippets called litmus test are generated, which show allowed or forbidden behaviour of different MCMs. This paper is showcasing the use of constraint programming to automate the generation and testing of litmus tests for memory consistency models. We produce a few exemplary case studies for two MCMs, namely Sequential Consistency and Total Store Order. These studies demonstrate the flexibility of constrains programming in this context and lay foundation to the direct verification of MCMs against the software facing cache coherence protocols.Postprin
Polynomial-Time Fence Insertion for Structured Programs
To enhance performance, common processors feature relaxed memory models that reorder instructions. However, the correctness of concurrent programs is often dependent on the preservation of the program order of certain instructions. Thus, the instruction set architectures offer memory fences. Using fences is a subtle task with performance and correctness implications: using too few can compromise correctness and using too many can hinder performance. Thus, fence insertion algorithms that given the required program orders can automatically find the optimum fencing can enhance the ease of programming, reliability, and performance of concurrent programs. In this paper, we consider the class of programs with structured branch and loop statements and present a greedy and polynomial-time optimum fence insertion algorithm. The algorithm incrementally reduces fence insertion for a control-flow graph to fence insertion for a set of paths. In addition, we show that the minimum fence insertion problem with multiple types of fence instructions is NP-hard even for straight-line programs
Deterministic Consistency: A Programming Model for Shared Memory Parallelism
The difficulty of developing reliable parallel software is generating
interest in deterministic environments, where a given program and input can
yield only one possible result. Languages or type systems can enforce
determinism in new code, and runtime systems can impose synthetic schedules on
legacy parallel code. To parallelize existing serial code, however, we would
like a programming model that is naturally deterministic without language
restrictions or artificial scheduling. We propose "deterministic consistency",
a parallel programming model as easy to understand as the "parallel assignment"
construct in sequential languages such as Perl and JavaScript, where concurrent
threads always read their inputs before writing shared outputs. DC supports
common data- and task-parallel synchronization abstractions such as fork/join
and barriers, as well as non-hierarchical structures such as producer/consumer
pipelines and futures. A preliminary prototype suggests that software-only
implementations of DC can run applications written for popular parallel
environments such as OpenMP with low (<10%) overhead for some applications.Comment: 7 pages, 3 figure
Recommended from our members
Automated generation of computationally hard feature models using evolutionary algorithms
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and
the Andalusian Government
Property-Driven Fence Insertion using Reorder Bounded Model Checking
Modern architectures provide weaker memory consistency guarantees than
sequential consistency. These weaker guarantees allow programs to exhibit
behaviours where the program statements appear to have executed out of program
order. Fortunately, modern architectures provide memory barriers (fences) to
enforce the program order between a pair of statements if needed. Due to the
intricate semantics of weak memory models, the placement of fences is
challenging even for experienced programmers. Too few fences lead to bugs
whereas overuse of fences results in performance degradation. This motivates
automated placement of fences. Tools that restore sequential consistency in the
program may insert more fences than necessary for the program to be correct.
Therefore, we propose a property-driven technique that introduces
"reorder-bounded exploration" to identify the smallest number of program
locations for fence placement. We implemented our technique on top of CBMC;
however, in principle, our technique is generic enough to be used with any
model checker. Our experimental results show that our technique is faster and
solves more instances of relevant benchmarks as compared to earlier approaches.Comment: 18 pages, 3 figures, 4 algorithms. Version change reason : new set of
results and publication ready version of FM 201
- …