Sequential and Parallel Improvements in a Concurrent Functional Programming Language

Sabel

2021

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In order to understand the relative expressive power of larger concurrent programming languages, we analyze translations of small process calculi which model the communication and synchronization of concurrent processes. The source language SYNCSIMPLE is a minimalistic model for message passing concurrency while the target language LOCKSIMPLE is a minimalistic model for shared memory concurrency. The former is a calculus with synchronous communication of processes, while the latter has synchronizing mutable locations -called locks -that behave similarly to binary semaphores. The criteria for correctness of translations is that they preserve and reflect maytermination and must-termination of the processes. We show that there is no correct compositional translation from SYNCSIMPLE to LOCKSIMPLE that uses one or two locks, independent from the initialisation of the locks. We also show that there is a correct translation that uses three locks. Also variants of the locks are taken into account with different blocking behavior.

Section: Introductionmentioning

confidence: 99%

Minimal Translations from Synchronous Communication to Synchronizing Locks

Sabel

2021

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“…Our goals are the comparison of programming languages, correctness of transformations, compilation and optimization of programs, in particular of concurrent programs. We already used the contextual semantics of concurrent (functional) programming languages to effectively verify correctness of transformations [16,23,24], also under the premise not to worsen the runtime [30]. We propose to test mayand should-convergence in the contextual semantics, since, in particular, it rules out transformations that transform an always successful process into a process that may run into an error, for example a deadlock.…”

Section: Introductionmentioning

confidence: 99%

Correctly Implementing Synchronous Message Passing in the Pi-Calculus By Concurrent Haskell's MVars

Sabel

2020

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Comparison of concurrent programming languages and correctness of program transformations in concurrency are the focus of this research. As criterion we use contextual semantics adapted to concurrency, where may-as well as should-convergence are observed. We investigate the relation between the synchronous pi-calculus and a core language of Concurrent Haskell (CH). The contextual semantics is on the one hand forgiving with respect to the details of the operational semantics, and on the other hand implies strong requirements for the interplay between the processes after translation. Our result is that CH embraces the synchronous pi-calculus. Our main task is to find and prove correctness of encodings of pi-calculus channels by CH's concurrency primitives, which are MVars. They behave like (blocking) 1-place buffers modelling the shared-memory. The first developed translation uses an extra private MVar for every communication. We also automatically generate and check potentially correct translations that reuse the MVars where one MVar contains the message and two additional MVars for synchronization are used to model the synchronized communication of a single channel in the pi-calculus. Our automated experimental results lead to the conjecture that one additional MVar is insufficient.

“…In addition to the program executions, it is crucial to recognize (binding-)garbage and remove it, since we are interested in space improving transformations. It is shown in [11,12] that garbage collection and the modification of the standard reduction (i.e. program execution) leaves all interesting properties (equivalence of expressions, correctness of transformations) invariant, and thus this is a correct and space-optimizing transformation.…”

Section: Introductionmentioning

confidence: 99%

“…P 1 starts with a local peak, hence we keep in mind 14 as the sum of the first elements and replace P 1 by P 1 = [1,12,5,7,1]. The next step is to apply the pattern M 3 , which reduce it to P 1 = [1,12,1]. Thus the new problem is P 1 = [1, 12, 1], P 2 = [3, 11, 2, 10, 3],…”

mentioning

confidence: 99%

Optimizing Space of Parallel Processes

Dallmeyer

2019

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This paper is a contribution to exploring and analyzing space-improvements in concurrent programming languages, in particular in the functional process-calculus CHF. Space-improvements are defined as a generalization of the corresponding notion in deterministic pure functional languages. The main part of the paper is the O(n · log n) algorithm SPOPTN for offline space optimization of several parallel independent processes. Applications of this algorithm are: (i) affirmation of space improving transformations for particular classes of program transformations; (ii) support of an interpreter-based method for refuting space-improvements; and (iii) as a stand-alone offline-optimizer for space (or similar resources) of parallel processes.• (independent) concurrent threads, independent of a programming language.Our model is also extended to synchronization constraints in the form of a Boolean combination of conditions on simultaneous and/or relative time points of two threads. The results for the space optimization for synchronization-free processes can be transferred to processes with synchronizations and permits polynomial algorithms for a fixed number of synchronization constructs (see Theorem 6.2) and therefore allows further analyses of space in more concrete scenarios. In general, i.e. for arbitrary Boolean constraints, finding the minimum is NP-complete (Theorem 6.4).The concrete programming language model that we investigate is the functional process calculus CHF, a variant of Concurrent Haskell, which permits pure and declarative functional modelling in combination with sequential (monadic) execution of processes with synchronization and which employs lazy evaluation [2,7,8]. Related work on space improvements in deterministic call-by-need functional languages is [5,6,10].An application of results and algorithms for the space-minimization task in special cases is on the one hand to identify program transformation as space improvements (in CHF) and on the other hand to accelerate an automated search for potential counterexamples to conjectures of space-improvements. Space optimization of parallel processes can sometimes be also applied to CHF-programs. For example for processes that are deterministically parallel, i.e. there is no sharing between processes, no free variables and the computation terminates. In these special cases the notion of space improvement is the same as space optimization.The structure of the paper is first to informally explain the functional process calculus CHF * GC and a definition of a space improvement in Section 2. A process-model and the interleaving is defined in Section 3. Then the computation of a standard form as a preparation of space optimization is given in Section 4. The optimization algorithm SPOPTN is defined in Section 5, where also the correctness and complexity are determined in Theorem 5.6. Extensions for synchronization constructs are in Section 6. Section 7 illustrates a relation to other scheduling methods and reports on an implementation and use of the algorithm. T...