arXiv subjects

Viktor Palmkvist

Publications and source records attributed to Viktor Palmkvist.

3 recordsLinked to original sources

arXiv2026

Reactive Graphs for Efficient Markov Chain Monte Carlo Inference in Probabilistic Programming Languages

An important aspect of making inference based on a probabilistic program practical is efficiency; faster evaluation enables more work per unit of time, which can be translated into more precision. Inference via Markov chain Monte Carlo has a property that can be favorably exploited for efficiency: most proposed samples are computed as minor variations of previous samples, i.e., a clever implementation can skip computations pertaining to what is unchanged. This paper provides an approach for automatically translating a probabilistic program to a dynamic graph, reminiscent of functional reactive programming, that explicitly represents data dependencies, enabling proposals to only recompute the parts of the graph that depend on redrawn random variables. The graph-building interface follows familiar functional programming interfaces, which also connect to their expressiveness in terms of probabilistic programming: models using the applicative functor portion express Bayesian networks, while those using monads represent universal probabilistic programming languages.

cs.PL↗

arXiv2024

Repr Types: One Abstraction to Rule Them All

The choice of how to represent an abstract type can have a major impact on the performance of a program, yet mainstream compilers cannot perform optimizations at such a high level. When dealing with optimizations of data type representations, an important feature is having extensible representation-flexible data types; the ability for a programmer to add new abstract types and operations, as well as concrete implementations of these, without modifying the compiler or a previously defined library. Many research projects support high-level optimizations through static analysis, instrumentation, or benchmarking, but they are all restricted in at least one aspect of extensibility. This paper presents a new approach to representation-flexible data types without such restrictions and which still finds efficient optimizations. Our approach centers around a single built-in type $\texttt{repr}$ and function overloading with cost annotations for operation implementations. We evaluate our approach (i) by defining a universal collection type as a library, a single type for all conventional collections, and (ii) by designing and implementing a representation-flexible graph library. Programs using $\texttt{repr}$ types are typically faster than programs with idiomatic representation choices -- sometimes dramatically so -- as long as the compiler finds good implementations for all operations. Our compiler performs the analysis efficiently by finding optimized solutions quickly and by reusing previous results to avoid recomputations.

cs.PL↗

arXiv2020

Resolvable Ambiguity

A common standpoint when designing the syntax of programming languages is that the grammar definition has to be unambiguous. However, requiring up front unambiguous grammars can force language designers to make more or less arbitrary choices to disambiguate the language. In this paper, we depart from the traditional view of unambiguous grammar design, and enable the detection of ambiguities to be delayed until parse time, allowing the user of the language to perform the disambiguation. A natural decision problem follows: given a language definition, can a user always disambiguate an ambiguous program? We introduce and formalize this fundamental problem - called the resolvable ambiguity problem - and divide it into separate static and dynamic resolvability problems. We provide solutions to the static problem for a restricted language class and sketch proofs of soundness and completeness. We also provide a sound and complete solution to the dynamic problem for a much less restricted class of languages. The approach is evaluated through two separate case studies, covering both a large existing programming language, and the composability of domain-specific languages.

cs.FL↗