| 1. |
- Eriksson, Oscar, et al.
(författare)
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Partial Evaluation of Automatic Differentiation for Differential-Algebraic Equations Solvers
- 2023
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Ingår i: GPCE 2023 - Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, Co-located with: SPLASH 2023. - : Association for Computing Machinery (ACM). ; , s. 57-71
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Konferensbidrag (refereegranskat)abstract
- Differential-Algebraic Equations (DAEs) are the foundation of high-level equation-based languages for modeling physical dynamical systems. Simulating models in such languages requires a transformation known as index reduction that involves differentiating individual equations before numerical integration. Commercial and open-source implementations typically perform index reduction by symbolic differentiation (SD) and produce a Jacobian callback function with forward-mode automatic differentiation (AD). The former results in efficient runtime code, and the latter is asymptotically efficient in both runtime and code size. However, AD introduces runtime overhead caused by a non-standard representation of real numbers, and SD is not always applicable in models with general recursion. This work proposes a new approach that uses partial evaluation of AD in the context of numerical DAE solving to combine the strengths of the two differentiation methods while mitigating their weaknesses. Moreover, our approach selectively specializes partial derivatives of the Jacobian by exploiting structural knowledge while respecting a user-defined bound on the code size. Our evaluation shows that the new method both enables expressive modeling from AD and retains the efficiency of SD for many practical applications.
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| 2. |
- Gomez Lopez, Etzar, et al.
(författare)
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Quantitative estimations of aquifer properties from resistivity in the Bolivian highlands
- 2019
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Ingår i: H2Open Journal. - : IWA Publishing. - 2616-6518. ; 2:1, s. 113-124
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Tidskriftsartikel (refereegranskat)abstract
- Resistivity data constitute the largest part of the available information to assess the hydrogeological characteristics of the aquifer system near Oruro, in the central part of the Bolivian Altiplano. Two aquifers are part of this system; top unconsolidated sediments storing fresh water in their granular voids, overlying fractured hard rock formations where saline water was detected in connection to some faults. This study proposes an indirect and cost-effective way to estimate aquifer hydraulic properties for the groundwater management in the region. Hydraulic conductivity and transmissivity in the top aquifer were estimated using an empirical linear relationship between hydraulic conductivity and resistivity. This latter parameter, as well as the aquifer thickness, were obtained from the inverted models corresponding to the geoelectrical tests performed in the study area (electrical resistivity tomography, transient electromagnetic soundings and vertical electrical soundings). The highest estimated transmissivity values are ∼4.0 × 10−2 m2/s located in the centre of the study area, the lowest values are ∼3.4 × 10−3 m2/s, located around thermal intrusions to the south and where the top of the bedrock is shallow (∼20 m depth) to the west. The methodology presented in this study makes wider use of resistivity measurements to identify promising groundwater production sites.
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| 3. |
- Kozma, Viktor, et al.
(författare)
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MORAP : a Modular Robotic Arm Platform for Teaching and Experimenting with Equation-based Modeling Languages [Work in Progress]
- 2016
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Ingår i: Proceedings of 7th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools (EOOLT). - New York, NY, USA : ACM Digital Library. - 9781450342025 ; , s. 27-30
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Konferensbidrag (refereegranskat)abstract
- Equation-based object-oriented (EOO) modeling and simu- lation techniques have in the last decades gained significant attention both in academia and industry. One of the key properties of EOO languages is modularity, where different components can be developed independently and then connected together to form a complete acausal model. However, extensive modeling without explicit model validation together with a real physical system can result in incorrect assumptions and false conclusions. In particular, in an educational and research setting, it is vital that students experiment both with equation-based models and the real system that is being modeled. In this work-in-progress paper, we present a physical experimental robotic arm platform that is designed for teaching and research. Similar to EOO models, the robotic arm is modular, meaning that its parts can be reconfigured and composed together in various settings, and used for different experiments. The platform is completely open source, where electronic schematics, CAD models for 3D printing, controller software, and component specifications are available on GitHub. The vision is to form a community, where new open source components are continuously added, to enable an open and freely available physical experimental platform for EOO languages.
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| 4. |
- Lundén, Daniel, 1993-, et al.
(författare)
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Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference
- 2022
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Ingår i: Programming Languages and Systems. - Cham : Springer. - 9783030993351 - 9783030993368 ; 13240, s. 29-56
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Konferensbidrag (refereegranskat)abstract
- Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking—as is done in many popular PPLs—often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs)—a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL—a high-level universal PPL—to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6 × speedups over state-of-the-art PPLs implementing SMC inference.
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| 5. |
- Palmkvist, Viktor, et al.
(författare)
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Creating domain-specific languages by composing syntactical constructs
- 2019
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Ingår i: 21st International Symposium on Practical Aspects of Declarative Languages, PADL 2019. - Cham : Springer. - 9783030059972 ; , s. 187-203
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Konferensbidrag (refereegranskat)abstract
- Creating a programming language is a considerable undertaking, even for relatively small domain-specific languages (DSLs). Most approaches to ease this task either limit the flexibility of the DSL or consider entire languages as the unit of composition. This paper presents a new approach using syntactical constructs (also called syncons) for defining DSLs in much smaller units of composition while retaining flexibility. A syntactical construct defines a single language feature, such as an if statement or an anonymous function. Each syntactical construct is fully self-contained: it specifies its own concrete syntax, binding semantics, and runtime semantics, independently of the rest of the language. The runtime semantics are specified as a translation to a user defined target language, while the binding semantics allow name resolution before expansion. Additionally, we present a novel approach for dealing with syntactical ambiguity that arises when combining languages, even if the languages are individually unambiguous. The work is implemented and evaluated in a case study, where small subsets of OCaml and Lua have been defined and composed using syntactical constructs.
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| 6. |
- Palmkvist, Viktor, et al.
(författare)
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Resolvable ambiguity : Principled resolution of syntactically ambiguous programs
- 2021
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Ingår i: CC 2021. - New York, NY, United States : ACM.
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Konferensbidrag (refereegranskat)abstract
- When building a new programming language, it can be useful to compose parts of existing languages to avoid repeating implementation work. However, this is problematic already at the syntax level, as composing the grammars of language fragments can easily lead to an ambiguous grammar. State-of-the-art parser tools cannot handle ambiguity truly well: either the grammar cannot be handled at all, or the tools give little help to an end-user who writes an ambiguous program. This composability problem is twofold: (i) how can we detect if the composed grammar is ambiguous, and (ii) if it is ambiguous, how can we help a user resolve an ambiguous program? In this paper, we depart from the traditional view of unambiguous grammar design and enable a language designer to work with an ambiguous grammar, while giving users the tools needed to handle these ambiguities. We introduce the concept of resolvable ambiguity wherein a user can resolve an ambiguous program by editing it, as well as an approach to computing the resolutions of an ambiguous program. Furthermore, we present a method based on property-based testing to identify if a composed grammar is unambiguous, resolvably ambiguous, or unresolvably ambiguous. The method is implemented in Haskell and evaluated on a large set of language fragments selected from different languages. The evaluation shows that (i) the approach can handle significantly more cases of language compositions compared to approaches which ban ambiguity altogether, and (ii) that the approach is fast enough to be used in practice.
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| 7. |
- Palmkvist, Viktor, et al.
(författare)
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Statically Resolvable Ambiguity
- 2023
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Ingår i: Proceedings of the ACM on Programming Languages. - : ACM Digital Library. - 2475-1421. ; 7:POPL, s. 1686-1712
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Tidskriftsartikel (refereegranskat)abstract
- Traditionally, a grammar defining the syntax of a programming language is typically both context free and unambiguous. However, recent work suggests that an attractive alternative is to use ambiguous grammars,thus postponing the task of resolving the ambiguity to the end user. If all programs accepted by an ambiguous grammar can be rewritten unambiguously, then the parser for the grammar is said to be resolvably ambiguous. Guaranteeing resolvable ambiguity statically---for all programs---is hard, where previous work only solves it partially using techniques based on property-based testing. In this paper, we present the first efficient, practical, and proven correct solution to the statically resolvable ambiguity problem. Our approach introduces several key ideas, including splittable productions, operator sequences, and the concept of a grouper that works in tandem with a standard parser. We prove static resolvability using a Coq mechanization and demonstrate its efficiency and practical applicability by implementing and integrating resolvable ambiguity into an essential part of the standard OCaml parser.
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| 8. |
- Ronquist, Fredrik, et al.
(författare)
-
Universal probabilistic programming offers a powerful approach to statistical phylogenetics
- 2021
-
Ingår i: Communications Biology. - : Springer Nature. - 2399-3642. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here, we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.
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| 9. |
- Ronquist, Fredrik, 1962-, et al.
(författare)
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Universal probabilistic programming offers a powerful approach to statistical phylogenetics
- 2021
-
Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 4:1
-
Tidskriftsartikel (refereegranskat)abstract
- Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here, we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.
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