Asynchronous functional reactive programming for GUIs

Czaplicki, Evan; Chong, Stephen

doi:10.1145/2491956.2462161

Cited by 138 publications

(115 citation statements)

References 33 publications

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“…Our implementation is a Scala port of an open-source Java implementation by Alan Hohn, 8 which is based on Doug Lea's fork/join framework [16]. Figure 11 shows the runtime of our implementations in relation to the number of used threads.…”

Section: Monte Carlo Net Present Value This Benchmark Computes the Nementioning

confidence: 99%

“…7 See https://en.wikipedia.org/wiki/Net present value. 8 See https://github.com/AlanHohn/monte-carlo-npv.…”

Section: Monte Carlo Net Present Value This Benchmark Computes the Nementioning

confidence: 99%

“…Languages like ELM [8], Scala.react [18], and ReactiveX/Rx [19] support asynchronous execution of reactive computations. Similar to cells, reactive abstractions support the composition of asynchronous computations.…”

Section: Related Workmentioning

confidence: 99%

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Reactive Async: expressive deterministic concurrency

Haller

Geries

Eichberg

et al. 2016

Proceedings of the 2016 7th ACM SIGPLAN Symposium on Scala

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Concurrent programming is infamous for its difficulty. An important source of difficulty is non-determinism, stemming from unpredictable interleavings of concurrent activities. Futures and promises are widely-used abstractions that help designing deterministic concurrent programs, although this property cannot be guaranteed statically in mainstream programming languages. Deterministic-by-construction concurrent programming models avoid this issue, but they typically restrict expressiveness in important ways.This paper introduces a concurrent programming model, Reactive Async, which decouples concurrent computations using so-called cells, shared locations which generalize futures as well as recent deterministic abstractions such as LVars. Compared to previously proposed programming models Reactive Async provides (a) a fallback mechanism for the case where no computation ever computes the value of a given cell, and (b) explicit and optimized handling of cyclic dependencies. We present a complete implementation of the Reactive Async programming model as a library in Scala. Finally, the paper reports on a case study applying Reactive Async to static analyses of JVM bytecode based on the Opal framework.

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Section: Monte Carlo Net Present Value This Benchmark Computes the Nementioning

confidence: 99%

“…7 See https://en.wikipedia.org/wiki/Net present value. 8 See https://github.com/AlanHohn/monte-carlo-npv.…”

Section: Monte Carlo Net Present Value This Benchmark Computes the Nementioning

confidence: 99%

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Reactive Async: expressive deterministic concurrency

Haller

Geries

Eichberg

et al. 2016

Proceedings of the 2016 7th ACM SIGPLAN Symposium on Scala

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“…[11] designed a type system in the Hoare-style on top of a stream language core as an example to demonstrate the expressiveness of their discrete-continuous transfer framework for verification. Elm [5] as a FRP-family language has a type system to outlaw higher-order signals. None of the related work above reasons about the rate of data processing.…”

Section: Related Workmentioning

confidence: 99%

“…A stream program consists of data-processing units (called filters) connected by paths to indicate the data flow. Stream programming -together with its close relatives of signal programming and (more generally) dataflow programming -is successful in scientific computing [1], graphics [2], databases [3], GUI design [4,5], robotics [6], sensor networks [7], and network switches [8]. Its growing popularity also generates significant interest in developing theoretical foundations for stream programming [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Rate types for stream programs

Bartenstein

Liu

2014

SIGPLAN Not.

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Abstract. We introduce RATE TYPES, a novel type system to reason about and optimize data-intensive programs. Built around stream languages, RATE TYPES performs static quantitative reasoning about stream rates -the frequency of data items in a stream being consumed, processed, and produced -a critical performance characteristic previously addressed by numerous experimental approaches but few foundational efforts. Even though streams are fundamentally dynamic, we find two essential concepts of stream rate control -throughput ratio and natural rate -are intimately related to the program structure itself and can be effectively reasoned about by a type system. RATE TYPES is proven sound over a time-aware and parallelism-aware operational semantics. The strong soundness result tolerates arbitrary schedules, and does not require any synchronization between stream filters. We further demonstrate the applications of RATE TYPES in energy-efficient computing and CPU allocation on multi-core architectures.

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Temporal Stream Logic: Synthesis Beyond the Bools

Finkbeiner

Klein

Piskač

et al. 2019

Computer Aided Verification

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Reactive systems that operate in environments with complex data, such as mobile apps or embedded controllers with many sensors, are difficult to synthesize. Synthesis tools usually fail for such systems because the state space resulting from the discretization of the data is too large. We introduce TSL, a new temporal logic that separates control and data. We provide a CEGAR-based synthesis approach for the construction of implementations that are guaranteed to satisfy a TSL specification for all possible instantiations of the data processing functions. TSL provides an attractive trade-off for synthesis. On the one hand, synthesis from TSL, unlike synthesis from standard temporal logics, is undecidable in general. On the other hand, however, synthesis from TSL is scalable, because it is independent of the complexity of the handled data. Among other benchmarks, we have successfully synthesized a music player Android app and a controller for an autonomous vehicle in the Open Race Car Simulator (TORCS). SynthesisLTL Circuit n FRP Translator Project Context Compiler LTL Synthesis Tool Counter Strategy Refinement unrealizable Design Pattern: Arrow | Applicative FRP EXE Function & Predicate Implementations FRP Library ✓ non-spurious spurious Sys . leaveApp () if ( MP . musicPlaying () ) Ctrl . pause () ; Sys . resumeApp () { pos = MP . trackPos () ; Ctrl . play ( Tr , pos ) ; } ALWAYS leaveApp Sys ∧ musicPlaying MP → Ctrl pause() ALWAYS resumeApp Sys → Ctrl play Tr (trackPos MP)

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Asynchronous functional reactive programming for GUIs

Cited by 138 publications

References 33 publications

Reactive Async: expressive deterministic concurrency

Reactive Async: expressive deterministic concurrency

Rate types for stream programs

Temporal Stream Logic: Synthesis Beyond the Bools

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