Selective Continuation-Passing Style Transformation for Efficient Suspension in Universal Probabilistic Programming Languages

Core Concepts

The core message of this paper is to develop a new efficient selective CPS approach for universal probabilistic programming languages. The authors design a novel static suspension analysis technique that determines the parts of programs that require suspension for a particular inference algorithm, and then selectively CPS transform the program only where necessary to reduce overhead from unnecessary closure allocations.

Abstract

The paper presents a new approach for efficiently enabling suspension in universal probabilistic programming languages (PPLs) that use Monte Carlo inference algorithms. Current state-of-the-art PPL implementations often rely on full continuation-passing style (CPS) transformations to enable suspension, which introduces overhead from unnecessary closure allocations.
The authors make the key observation that PPLs do not require arbitrary suspensions provided by full CPS transformations - most Monte Carlo inference algorithms only require suspension in specific parts of the program. They develop a static suspension analysis that determines the parts of the program that may require suspension for a given inference algorithm. This analysis then guides a selective CPS transformation, where only the necessary parts of the program are transformed to enable suspension.
The suspension analysis is formalized using a core PPL calculus and proven correct. The selective CPS transformation is also formalized and shown to significantly reduce runtime overhead compared to a full CPS transformation, while still enabling the suspensions required by the inference algorithm.
The authors implement the suspension analysis and selective CPS transformation in the Miking CorePPL compiler. They evaluate the approach on a range of real-world models and inference algorithms, demonstrating significant performance improvements across all cases.

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Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

by Dani... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2302.13051.pdf

Deeper Inquiries

What are some potential limitations or challenges of the suspension analysis and selective CPS transformation approach

One potential limitation of the suspension analysis and selective CPS transformation approach is the conservative nature of the analysis. Since the analysis is based on 0-CFA, it may overestimate the suspension requirements in the program, leading to unnecessary CPS transformations. This can result in additional complexity in the transformed program and potentially impact performance. Additionally, the selective CPS transformation relies heavily on the accuracy of the suspension analysis. If the analysis does not accurately identify the parts of the program that require suspension, the selective transformation may not optimize the program effectively.
Another challenge is the scalability of the approach. As the size and complexity of the probabilistic programs increase, the analysis and transformation process may become computationally intensive. This could limit the applicability of the techniques to large-scale or real-world applications where efficiency is crucial.

How could the techniques presented in this paper be extended or applied to other domains beyond probabilistic programming

The techniques presented in this paper could be extended or applied to other domains beyond probabilistic programming. For example, the suspension analysis and selective CPS transformation approach could be adapted for use in other programming paradigms that involve suspension or continuation-passing style, such as concurrent programming, event-driven programming, or reactive programming. By identifying parts of the program that require suspension and selectively transforming them, the approach could help optimize the performance of programs in these domains as well.
Furthermore, the concepts of suspension analysis and selective CPS transformation could be applied to optimize resource management in systems programming, where efficient handling of suspensions and continuations is essential for memory and resource utilization. By tailoring the techniques to suit the specific requirements of these domains, it is possible to improve the overall performance and efficiency of programs in various application areas.

Are there any alternative approaches to enabling suspension in PPLs that could be explored and compared to the selective CPS transformation

One alternative approach to enabling suspension in probabilistic programming languages (PPLs) that could be explored and compared to the selective CPS transformation is the use of delimited continuations. Delimited continuations provide a more fine-grained control over the scope of continuations, allowing for more precise suspension and resumption of computations. By leveraging delimited continuations, PPLs could potentially achieve more targeted suspension points without the need for a full CPS transformation.
Another approach worth exploring is the integration of lightweight concurrency mechanisms, such as async/await syntax or coroutines, to handle suspension in PPLs. By incorporating these mechanisms into the language design, PPLs can achieve efficient suspension and resumption of computations without the overhead associated with traditional CPS transformations. This approach could offer a more lightweight and flexible solution for managing suspensions in probabilistic programming.