Deterministic Consistency: A Programming Model for Shared Memory Parallelism

The difficulty of developing reliable parallel software is generating interest in deterministic environments, where a given program and input can yield only one possible result. Languages or type systems can enforce determinism in new code, and runtime systems can impose synthetic schedules on legacy parallel code. To parallelize existing serial code, however, we would like a programming model that is naturally deterministic without language restrictions or artificial scheduling. We propose "deterministic consistency", a parallel programming model as easy to understand as the "parallel assignment" construct in sequential languages such as Perl and JavaScript, where concurrent threads always read their inputs before writing shared outputs. DC supports common data- and task-parallel synchronization abstractions such as fork/join and barriers, as well as non-hierarchical structures such as producer/consumer pipelines and futures. A preliminary prototype suggests that software-only implementations of DC can run applications written for popular parallel environments such as OpenMP with low (<10%) overhead for some applications.Comment: 7 pages, 3 figure

Multiprocess parallel antithetic coupling for backward and forward Markov Chain Monte Carlo

Antithetic coupling is a general stratification strategy for reducing Monte Carlo variance without increasing the simulation size. The use of the antithetic principle in the Monte Carlo literature typically employs two strata via antithetic quantile coupling. We demonstrate here that further stratification, obtained by using k>2 (e.g., k=3-10) antithetically coupled variates, can offer substantial additional gain in Monte Carlo efficiency, in terms of both variance and bias. The reason for reduced bias is that antithetically coupled chains can provide a more dispersed search of the state space than multiple independent chains. The emerging area of perfect simulation provides a perfect setting for implementing the k-process parallel antithetic coupling for MCMC because, without antithetic coupling, this class of methods delivers genuine independent draws. Furthermore, antithetic backward coupling provides a very convenient theoretical tool for investigating antithetic forward coupling. However, the generation of k>2 antithetic variates that are negatively associated, that is, they preserve negative correlation under monotone transformations, and extremely antithetic, that is, they are as negatively correlated as possible, is more complicated compared to the case with k=2. In this paper, we establish a theoretical framework for investigating such issues. Among the generating methods that we compare, Latin hypercube sampling and its iterative extension appear to be general-purpose choices, making another direct link between Monte Carlo and quasi Monte Carlo.Comment: Published at http://dx.doi.org/10.1214/009053604000001075 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fault tolerant architectures for integrated aircraft electronics systems

Work into possible architectures for future flight control computer systems is described. Ada for Fault-Tolerant Systems, the NETS Network Error-Tolerant System architecture, and voting in asynchronous systems are covered

08371 Abstracts Collection -- Fault-Tolerant Distributed Algorithms on VLSI Chips

From September the $7^{text{th}}$, 2008 to September the $10^{text{th}}$, 2008 the Dagstuhl Seminar 08371 ``Fault-Tolerant Distributed Algorithms on VLSI Chips \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The seminar was devoted to exploring whether the wealth of existing fault-tolerant distributed algorithms research can be utilized for meeting the challenges of future-generation VLSI chips. During the seminar, several participants from both the VLSI and distributed algorithms\u27 discipline, presented their current research, and ongoing work and possibilities for collaboration were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Enabling distributed analysis for ALICE Run 3

The ALICE Collaboration has just finished a major detector upgrade that increases the data-taking rate capability by two orders of magnitude and will allow to collect unprecedented data samples. For example, the analysis input for 1 month of Pb-Pb collisions amounts to about 5 PB. In order to enable analysis on such large data samples, the ALICE distributed infrastructure was revised and dedicated tools for Run 3 analysis were created. These are firstly the $\mathrm{O^2}$ analysis framework that builds on a multi-process architecture exchanging a flat data format through shared memory implemented in C++. Secondly, the Hyperloop train system for distributed analysis on the Grid and on dedicated analysis facilities implemented in Java/Javascript/React. These systems have been commissioned with converted Run 2 data and with the recent LHC pilot beam and are ready for data analysis for the start of Run 3. This contribution discusses the requirements and the used concepts, providing details on the actual implementation. The status of the operation in particular with respect to the LHC pilot beam will also be discussed.Comment: Contribution to the proceedings of the 41st International Conference on High Energy Physics (ICHEP2022), 6-13 July 2022, Bologna, Italy. Contains: 6 pages, 7 figure