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1. Efficient, Portable, Census-Polymorphic Choreographic Programming

   https://doi.org/10.1145/3729296  

   Bates, Mako; Kashiwa, Shun; Jafri, Syed; Shen, Gan; Kuper, Lindsey; Near, Joseph P (June 2025, Proceedings of the ACM on Programming Languages)

   Choreographic programming (CP) is a paradigm for implementing distributed systems that uses a single global program to define the actions and interactions of all participants. Library-level CP implementations, like HasChor, integrate well with mainstream programming languages but have several limitations: Their conditionals require extra communication; they require specific host-language features (e.g., monads); and they lack support for programming patterns that are essential for implementing realistic distributed applications. We make three contributions to library-level CP to specifically address these challenges. First, we propose and formalizeconclavesandmultiply-located values, which enable efficient conditionals in library-level CP without redundant communication. Second, we proposecensus polymorphism, a technique for abstracting over the number of participants in a choreography. Third, we introduce a design pattern for library-level CP in host languages without support for monads. We demonstrate these contributions via implementations in Haskell, Rust, and TypeScript. 

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2. Explainable dynamic programming

   https://doi.org/10.1017/S0956796821000083  

   ERWIG, MARTIN; KUMAR, PRASHANT (January 2021, Journal of Functional Programming)

   null (Ed.)

   Abstract In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one result was obtained instead of another and therefore can increase the confidence in the correctness of program results. Our focus on dynamic programming is motivated by the fact that dynamic programming offers a systematic approach to implementing a large class of optimization algorithms which produce decisions based on aggregated value comparisons. It is those decisions that the granular representation can help explain. Moreover, the fact that dynamic programming can be formalized using semirings supports the creation of a Haskell library for dynamic programming that has two important features. First, it allows programmers to specify programs by recurrence relationships from which efficient implementations are derived automatically. Second, the dynamic programs can be formulated generically (as type classes), which supports the smooth transition from programs that only produce result to programs that can run with granular representation and also produce explanations. Finally, we also demonstrate how to anticipate user questions about program results and how to produce corresponding explanations automatically in advance. 

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3. Searching entangled program spaces

   https://doi.org/10.1145/3547622  

   Koppel, James; Guo, Zheng; de_Vries, Edsko; Solar-Lezama, Armando; Polikarpova, Nadia (August 2022, Proceedings of the ACM on Programming Languages)

   Many problem domains, including program synthesis and rewrite-based optimization, require searching astronomically large spaces of programs. Existing approaches often rely on building specialized data structures—version-space algebras, finite tree automata, or e-graphs—to compactly represent such spaces. At their core, all these data structures exploit independence of subterms; as a result, they cannot efficiently represent more complex program spaces, where the choices of subterms are entangled. We introduceequality-constrained tree automata(ECTAs), a new data structure, designed to compactly represent large spaces of programs with entangled subterms. We present efficient algorithms for extracting programs from ECTAs, implemented in a performant Haskell library, ecta. Using the ecta library, we construct Hectare, a type-driven program synthesizer for Haskell. Hectare significantly outperforms a state-of-the-art synthesizer Hoogle+—providing an average speedup of 8×—despite its implementation being an order of magnitude smaller. 

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4. Total Haskell is reasonable Coq

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   Spector-Zabusky, Antal; Breitner, Joachim; Rizkallah, Christine; Weirich, Stephanie (January 2018, Proceeding CPP 2018 Proceedings of the 7th ACM SIGPLAN International Conference on Certified Programs and Proofs)

   We would like to use the Coq proof assistant to mechanically verify properties of Haskell programs. To that end, we present a tool, named hs-to-coq, that translates total Haskell programs into Coq programs via a shallow embedding. We apply our tool in three case studies -- a lawful Monad instance, ``Hutton's razor'', and an existing data structure library -- and prove their correctness. These examples show that this approach is viable: both that hs-to-coq applies to existing Haskell code, and that the output it produces is amenable to verification. 

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5. Provably space-efficient parallel functional programming

   https://doi.org/10.1145/3434299  

   Arora, Jatin; Westrick, Sam; Acar, Umut A. (January 2021, Proceedings of the ACM on Programming Languages)

   null (Ed.)

   Because of its many desirable properties, such as its ability to control effects and thus potentially disastrous race conditions, functional programming offers a viable approach to programming modern multicore computers. Over the past decade several parallel functional languages, typically based on dialects of ML and Haskell, have been developed. These languages, however, have traditionally underperformed procedural languages (such as C and Java). The primary reason for this is their hunger for memory, which only grows with parallelism, causing traditional memory management techniques to buckle under increased demand for memory. Recent work opened a new angle of attack on this problem by identifying a memory property of determinacy-race-free parallel programs, called disentanglement, which limits the knowledge of concurrent computations about each other’s memory allocations. The work has showed some promise in delivering good time scalability. In this paper, we present provably space-efficient automatic memory management techniques for determinacy-race-free functional parallel programs, allowing both pure and imperative programs where memory may be destructively updated. We prove that for a program with sequential live memory of R * , any P -processor garbage-collected parallel run requires at most O ( R * · P ) memory. We also prove a work bound of O ( W + R * P ) for P -processor executions, accounting also for the cost of garbage collection. To achieve these results, we integrate thread scheduling with memory management. The idea is to coordinate memory allocation and garbage collection with thread scheduling decisions so that each processor can allocate memory without synchronization and independently collect a portion of memory by consulting a collection policy, which we formulate. The collection policy is fully distributed and does not require communicating with other processors. We show that the approach is practical by implementing it as an extension to the MPL compiler for Parallel ML. Our experimental results confirm our theoretical bounds and show that the techniques perform and scale well. 

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