Concurrent goal-based execution of Constraint Handling Rules

Lam, Edmund S. L.; Sulzmann, Martin

doi:10.1017/s147106841000044x

Cited by 4 publications

(5 citation statements)

References 17 publications

(35 reference statements)

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“…The first example of parallel implementation was due to Frühwirth [9] where it was shown how to evaluate the degree of concurrency starting from the confluence analysis of a sequential program execution. Further works by Sulzmann and Lam [16,28] focus on the formal specification and the development of a parallel implementation of the CHR goal-based execution schema: multiple processor cores run multiple threads following a single CHR goal. Other attempts to exploit concurrency in CHR were pursued in the last years, mainly driven by the CHR set-based operational semantics [21].…”

Section: Related Workmentioning

confidence: 99%

Compiling CHR to parallel hardware

Triossi

Orlando

Raffaetà

et al. 2012

Proceedings of the 14th Symposium on Principles and Practice of Declarative Programming

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This paper investigates the compilation of a committed-choice rulebased language, Constraint Handling Rules (CHR), to specialized hardware circuits. The developed hardware is able to turn the intrinsic concurrency of the language into parallelism. Rules are applied by a custom executor that handles constraints according to the best degree of parallelism the implemented CHR specification can offer.Our framework deploys the target digital circuits through the Field Programmable Gate Array (FPGA) technology, by first compiling the CHR code fragment into a low level hardware description language.We also discuss the realization of a hybrid CHR interpreter, consisting of a software component running on a general purpose processor, coupled with a hardware accelerator. The latter unburdens the processor by executing in parallel the most computational intensive CHR rules directly compiled in hardware. Finally the performance of a prototype system is evaluated by time efficiency measures.

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Section: Related Workmentioning

confidence: 99%

Compiling CHR to parallel hardware

Triossi

Orlando

Raffaetà

et al. 2012

Proceedings of the 14th Symposium on Principles and Practice of Declarative Programming

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“…The following results are proven in the appendix of Lam and Sulzmann (2009). Monotonicity holds for the goal store, but not for the constraint store.…”

Section: Properties: Monotonicity Soundness and Serializabilitymentioning

confidence: 94%

“…A refined semantics for parallel CHR is developed and implemented in , Lam and Sulzmann (2009), and Lam (2018). This semantics can be seen as a refinement of the parallel abstract semantics given before.…”

Section: Refined Parallel Chr Semanticsmentioning

confidence: 99%

Parallelism, concurrency and distribution in constraint handling rules: A survey

Frühwirth¹

2018

Theory and Practice of Logic Programming

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Constraint Handling Rules (CHR) is both an effective concurrent declarative programming language and a versatile computational logic formalism. In CHR, guarded reactive rules rewrite a multi-set of constraints. Concurrency is inherent, since rules can be applied to the constraints in parallel. In this comprehensive survey, we give an overview of the concurrent, parallel as well as distributed CHR semantics, standard and more exotic, that have been proposed over the years at various levels of refinement. These semantics range from the abstract to the concrete. They are related by formal soundness results. Their correctness is proven as a correspondence between parallel and sequential computations. On the more practical side, we present common concise example CHR programs that have been widely used in experiments and benchmarks. We review parallel and distributed CHR implementations in software as well as hardware. The experimental results obtained show a parallel speed-up for unmodified sequential CHR programs. The software implementations are available online for free download and we give the web links. Due to its high level of abstraction, the CHR formalism can also be used to implement and analyse models for concurrency. To this end, the Software Transaction Model, the Actor Model, Colored Petri Nets and the Join-Calculus have been faithfully encoded in CHR. Finally, we identify and discuss commonalities of the approaches surveyed and indicate what problems are left open for future research.

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“…LM still follows the same graph model of computation of Meld, which makes LM programs naturally concurrent since the graph of nodes can be easily partitioned to be executed by different threads. As a forward-chaining linear logic programming language, LM also shares similarities with Constraint Handling Rules (CHR) [6,22]. CHR is a concurrent committed-choice constraint language used to write constraint solvers.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm uses three rules for the three cases of updating a key's value: the first rule performs the update (lines 6-9); the second rule recursively picks the left branch for the update operation (lines [11][12][13][14][15][16]; and the third rule picks the right branch (lines [18][19][20][21][22][23]. The initial axioms are presented in lines 26-33 and they describe the initial binary tree configuration, including keys and values.…”

mentioning

confidence: 99%

Design and Implementation of a Multithreaded Virtual Machine for Executing Linear Logic Programs

Cruz

Rocha

Goldstein

2014

Proceedings of the 16th International Symposium on Principles and Practice of Declarative Programming

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Linear Meld is a concurrent forward-chaining linear logic programming language where logical facts can be asserted and retracted in a structured way. In Linear Meld, a program is seen as a database of logical facts and a set of derivation rules. The database of facts is partitioned by the nodes of a graph structure which leads to parallelism when nodes are executed simultaneously. Due to the foundations on linear logic, rules can retract facts in a declarative and structured fashion, leading to more expressive programs. We present the design and implementation of the virtual machine that we implemented to run Linear Meld on multicores, with particular focus on thread management, code organization, fact indexing, rule execution, and database organization for efficient fact insertion, lookup and deletion. Our results show that the virtual machine is capable of scaling programs with up to 16 threads and also exhibits interesting scalar performance results due to our indexing optimizations.

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Concurrent goal-based execution of Constraint Handling Rules

Cited by 4 publications

References 17 publications

Compiling CHR to parallel hardware

Compiling CHR to parallel hardware

Parallelism, concurrency and distribution in constraint handling rules: A survey

Design and Implementation of a Multithreaded Virtual Machine for Executing Linear Logic Programs

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