arXiv subjects

David Broman

Publications and source records attributed to David Broman.

30 records · Page 2Linked to original sources

arXiv2023

Automatic Alignment in Higher-Order Probabilistic Programming Languages

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints -- relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run -- they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.

cs.PL↗

arXiv2023

Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control

We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC is used in real time to control safety-critical systems. The proposed method applies when the quadratic programming solver in the MPC controller belongs to a family of well-established active-set solvers. For such solvers, we leverage a previously proposed complexity certification framework to generate a finite set of archetypal optimization problems; we prove that these archetypal problems form an execution-time equivalent cover of all possible problems; that is, that they capture the execution time for solving any possible optimization problem that can be encountered online. Hence, by solving just these archetypal problems on the hardware on which the MPC is to be deployed, and by recording the execution times, we obtain the exact WCET. In addition to providing formal proofs of the methods efficacy, we validate the method on an MPC example where an inverted pendulum on a cart is stabilized. The experiments highlight the following advantages compared with classical WCET methods: (i) in contrast to classical static methods, our method gives the exact WCET; (ii) in contrast to classical measurement-based methods, our method guarantees a correct WCET estimate and requires fewer measurements on the hardware.

eess.SY↗

arXiv2022

Programming with Context-Sensitive Holes using Dependency-Aware Tuning

Developing efficient and maintainable software systems is both hard and time consuming. In particular, non-functional performance requirements involve many design and implementation decisions that can be difficult to take early during system development. Choices -- such as selection of data structures or where and how to parallelize code -- typically require extensive manual tuning that is both time consuming and error-prone. Although various auto-tuning approaches exist, they are either specialized for certain domains or require extensive code rewriting to work for different contexts in the code. In this paper, we introduce a new methodology for writing programs with holes, that is, decision variables explicitly stated in the program code that enable developers to postpone decisions during development. We introduce and evaluate two novel ideas: (i) context-sensitive holes that are expanded by the compiler into sets of decision variables for automatic tuning, and (ii) dependency-aware tuning, where static analysis reduces the search space by finding the set of decision variables that can be tuned independently of each other. We evaluate the two new concepts in a system called Miking, where we show how the general methodology can be used for automatic algorithm selection, data structure decisions, and parallelization choices.

cs.PL↗

arXiv2021

Automatic Diversity in the Software Supply Chain

Despite its obvious benefits, the increased adoption of package managers to automate the reuse of libraries has opened the door to a new class of hazards: supply chain attacks. By injecting malicious code in one library, an attacker may compromise all instances of all applications that depend on the library. To mitigate the impact of supply chain attacks, we propose the concept of Library Substitution Framework. This novel concept leverages one key observation: when an application depends on a library, it is very likely that there exists other libraries that provide similar features. The key objective of Library Substitution Framework is to enable the developers of an application to harness this diversity of libraries in their supply chain. The framework lets them generate a population of application variants, each depending on a different alternative library that provides similar functionalities. To investigate the relevance of this concept, we develop ARGO, a proof-of-concept implementation of this framework that harnesses the diversity of JSON suppliers. We study the feasibility of library substitution and its impact on a set of 368 clients. Our empirical results show that for 195 of the 368 java applications tested, we can substitute the original JSON library used by the client by at least 15 other JSON libraries without modifying the client's code. These results show the capacity of a Library Substitution Framework to diversify the supply chain of the client applications of the libraries it targets.

cs.SE↗

arXiv2021

The Behavioral Diversity of Java JSON Libraries

JSON is an essential file and data format in do-mains that span scientific computing, web APIs or configuration management. Its popularity has motivated significant software development effort to build multiple libraries to process JSON data. Previous studies focus on performance comparison among these libraries and lack a software engineering perspective.We present the first systematic analysis and comparison of the input / output behavior of 20 JSON libraries, in a single software ecosystem: Java/Maven. We assess behavior diversity by running each library against a curated set of 473 JSON files, including both well-formed and ill-formed files. The main design differences, which influence the behavior of the libraries, relate to the choice of data structure to represent JSON objects and to the encoding of numbers. We observe a remarkable behavioral diversity with ill-formed files, or corner cases such as large numbers or duplicate data. Our unique behavioral assessment of JSON libraries paves the way for a robust processing of ill-formed files, through a multi-version architecture.

cs.SE↗

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↗

arXiv2018

Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs

Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequential Monte Carlo inference is the alignment of program synchronization points. We propose a new static analysis approach based on the 0-CFA algorithm for automatically aligning higher-order probabilistic programs. We evaluate the automatic alignment on a phylogenetic model, showing a significant decrease in runtime and increase in accuracy.

cs.PL↗

arXiv2018

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.

stat.ML↗

arXiv2017

A Brief Overview of the KTA WCET Tool

KTA (KTH's timing analyzer) is a research tool for performing timing analysis of program code. The currently available toolchain can perform two different kinds of analyses: i) exhaustive fine-grained timing analysis, where timing information can be provided between arbitrary timing program points within a function, and ii) abstract search-based timing analysis, where the tool can perform optimal worst-case execution time (WCET) analysis. The latter is based on a technique that combines divide-and-conquer search and abstract interpretation. The tool is under development and currently supports a subset of the MIPS instruction set architecture.

cs.SE↗

arXiv2017

Hybrid Simulation Safety: Limbos and Zero Crossings

Physical systems can be naturally modeled by combining continuous and discrete models. Such hybrid models may simplify the modeling task of complex system, as well as increase simulation performance. Moreover, modern simulation engines can often efficiently generate simulation traces, but how do we know that the simulation results are correct? If we detect an error, is the error in the model or in the simulation itself? This paper discusses the problem of simulation safety, with the focus on hybrid modeling and simulation. In particular, two key aspects are studied: safe zero-crossing detection and deterministic hybrid event handling. The problems and solutions are discussed and partially implemented in Modelica and Ptolemy II.

cs.CY↗

arXiv2017

Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models

Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler.

stat.ML↗

arXiv2017

Co-simulation: State of the art

It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures. One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system. Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings. Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area. We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years. The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct.

eess.SY↗