Consistency model

Abstract: Scalable shared-memory multiprocessors distribute memory among the processors and use scalable interconnection networks to provide high bandwidth and low latency communication. In addition, memory accesses are cached, buffered, and pipelined to bridge the gap between the slow shared memory and the fast processors. Unless carefully controlled, such architectural optimizations can cause memory accesses to be executed in an order different from what the programmer expects. The set of allowable memory access orderings forms the memory consistency model or event ordering model for an architecture.This paper introduces a new model of memory consistency, called release consistency, that allows for more buffering and pipelining than previously proposed models. A framework for classifying shared accesses and reasoning about event ordering is developed. The release consistency model is shown to be equivalent to the sequential consistency model for parallel programs with sufficient synchronization. Possible performance gains from the less strict constraints of the release consistency model are explored. Finally, practical implementation issues are discussed, concentrating on issues relevant to scalable architectures.

Abstract: The memory consistency model of a system affects performance, programmability, and portability. We aim to describe memory consistency models in a way that most computer professionals would understand. This is important if the performance-enhancing features being incorporated by system designers are to be correctly and widely used by programmers. Our focus is consistency models proposed for hardware-based shared memory systems. Most of these models emphasize the system optimizations they support, and we retain this system-centric emphasis. We also describe an alternative, programmer-centric view of relaxed consistency models that describes them in terms of program behavior, not system optimizations.

Abstract: We propose a parameter server framework for distributed machine learning problems. Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices. The framework manages asynchronous data communication between nodes, and supports flexible consistency models, elastic scalability, and continuous fault tolerance.To demonstrate the scalability of the proposed framework, we show experimental results on petabytes of real data with billions of examples and parameters on problems ranging from Sparse Logistic Regression to Latent Dirichlet Allocation and Distributed Sketching.

Abstract: The most common metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and generated summaries. Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly for three tasks: 1) predict whether each summary sentence is factually consistent or not, 2) in either case, extract a span in the source document to support this consistency prediction, 3) for each summary sentence that is deemed inconsistent, extract the inconsistent span from it. Transferring this model to summaries generated by several neural models reveals that this highly scalable approach outperforms previous models, including those trained with strong supervision using datasets from related domains, such as natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency. We also release a manually annotated dataset for factual consistency verification, code for training data generation, and trained model weights at https://github.com/salesforce/factCC.