scholarly journals Statically bounded-memory delayed sampling for probabilistic streams

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-28
Author(s):  
Eric Atkinson ◽  
Guillaume Baudart ◽  
Louis Mandel ◽  
Charles Yuan ◽  
Michael Carbin
Keyword(s):  
Bayesian Inference ◽  
Programming Languages ◽  
Static Analysis ◽  
Programming Language ◽  
Inference Algorithm ◽  
Prior Work ◽  
Probabilistic Programming ◽  
The Core ◽  
Bounded Memory ◽  
Automated Inference

Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we the present conditions on a probabilistic program’s execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m -consumed property and the unseparated paths property . A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m -consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.

scholarly journals A Mechanized Proof of a Textbook Type Unification Algorithm

2020 ◽  
Vol 27 (3) ◽  
pp. 13-24
Author(s):  
André Rauber Du Bois ◽  
Rodrigo Ribeiro ◽  
Maycon Amaro
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Functional Programming ◽  
Type Inference ◽  
Inference Algorithm ◽  
Functional Programming Languages ◽  
Unification Algorithm ◽  
The Core ◽  
Inference Algorithms ◽  
Language Textbooks

Unification is the core of type inference algorithms for modern functional programming languages, like Haskell and SML. As a first step towards a formalization of a type inference algorithm for such programming languages, we present a formalization in Coq of a type unification algorithm that follows classic algorithms presented in programming language textbooks. We also report on the use of such formalization to build a correct type inference algorithm for the simply typed λ-calculus.

scholarly journals Static Validation of XSL Transformations

2005 ◽  
Vol 12 (32) ◽  
Author(s):  
Anders Møller ◽  
Mads Østerby Olesen ◽  
Michael I. Schwartzbach
Keyword(s):  
Web Services ◽  
Programming Languages ◽  
Large Class ◽  
Static Analysis ◽  
Programming Language ◽  
Pragmatic Approach ◽  
Validation Technique ◽  
Summary Graph

XSL Transformations (XSLT) is a programming language for defining transformations between XML languages. The structure of these languages is formally described by schemas, for example using DTD, which allows individual documents to be validated. However, existing XSLT tools offer no static guarantees that, under the assumption that the input is valid relative to the input schema, the output of the transformation is valid relative to the output schema.<br /> <br />We present a validation technique for XSLT based on the summary graph formalism introduced in the static analysis of JWIG Web services. Being able to provide static guarantees, we can detect a large class of errors in an XSLT stylesheet at the time it is written instead of later when it has been deployed, and thereby provide benefits similar to those of static type checkers for modern programming languages.<br /> <br />Our analysis takes a pragmatic approach that focuses its precision on the essential language features but still handles the entire XSLT 1.0 language. We evaluate the analysis precision on a range of real stylesheets and demonstrate how it may be useful in practice.

scholarly journals Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

2019 ◽  
Vol 20 (1) ◽  
pp. 147-175 ◽  
Author(s):  
SANDRA DYLUS ◽  
JAN CHRISTIANSEN ◽  
FINN TEEGEN
Keyword(s):  
Logic Programming ◽  
Programming Languages ◽  
Programming Language ◽  
Probabilistic Choice ◽  
Probabilistic Programming ◽  
Logic Programming Language ◽  
Functional Logic Programming ◽  
Functional Logic ◽  
Language Characteristics ◽  
Standard List

AbstractThis paper presentsPFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, language characteristics from one area exist in the other and vice versa. For example, the concepts of non-deterministic choice and call-time choice as known from functional logic programming are related to and coincide with stochastic memoization and probabilistic choice in probabilistic programming, respectively. We will further see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation and can even compete with full-blown probabilistic programming languages, which we illustrate by several benchmarks.

scholarly journals Estimating Ground-Motion Models via Bayesian Inference using Stan

2021 ◽  
Author(s):  
Nicolas Kuehn ◽  
Peter Stafford
Keyword(s):  
Bayesian Inference ◽  
Ground Motion ◽  
Programming Language ◽  
Motion Model ◽  
Probabilistic Programming ◽  
Motion Models ◽  
Ground Motion Model ◽  
Computer Environment ◽  
Ground Motion Models

We provide a simple introduction to the estimation of ground-motion models via Bayesian inference and the probabilistic programming language Stan.We show one ca implement a simple ground-motion model in Stan, and how can run the program from the computer environment R.We also show how one can access the results, and plot summaries of estimated parameters.A large number of different Stan models for the development https://github.com/pstafford/StanGMMTutorial.

PERANGKAT LUNAK KOMPUTER

2020 ◽  
Author(s):  
Cut Nabilah Damni
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Operating Systems ◽  
Source Code ◽  
Computer Software ◽  
Computer Programs ◽  
Application Systems ◽  
Executable Programs

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, application systems and programming languages.

scholarly journals Investigation of Data Dependencies by Dynamic Analysis of Sapfor

2020 ◽  
Vol 23 (3) ◽  
pp. 473-493
Author(s):  
Nikita Andreevich Kataev ◽  
Alexander Andreevich Smirnov ◽  
Andrey Dmitrievich Zhukov
Keyword(s):  
Dynamic Analysis ◽  
Programming Languages ◽  
Static Analysis ◽  
Automatic Parallelization ◽  
Parallel Execution ◽  
Control Flow ◽  
Analysis Tool ◽  
Data Dependencies ◽  
Program Parallelization ◽  
Memory Accesses

The use of pointers and indirect memory accesses in the program, as well as the complex control flow are some of the main weaknesses of the static analysis of programs. The program properties investigated by this analysis are too conservative to accurately describe program behavior and hence they prevent parallel execution of the program. The application of dynamic analysis allows us to expand the capabilities of semi-automatic parallelization. In the SAPFOR system (System FOR Automated Parallelization), a dynamic analysis tool has been implemented, based on on the instrumentation of the LLVM representation of an analyzed program, which allows the system to explore programs in both C and Fortran programming languages. The capabilities of the static analysis implemented in SAPFOR are used to reduce the overhead program execution, while maintaining the completeness of the analysis. The use of static analysis allows to reduce the number of analyzed memory accesses and to ignore scalar variables, which can be explored in a static way. The developed tool was tested on performance tests from the NAS Parallel Benchmarks package for C and Fortran languages. The implementation of dynamic analysis, in addition to traditional types of data dependencies (flow, anit, output), allows us to determine privitizable variables and a possibility of pipeline execution of loops. Together with the capabilities of DVM and OpenMP these greatly facilitates program parallelization and simplify insertion of the appropriate compiler directives.

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