TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training), then with applying those functions (during inference). These functions are defined as compositions of simple primitives (as is common in functional programming), with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.

We study the interaction of the "new" construct with a rich but common form of (first-order) communication. This interaction is crucial in security protocols, which are the main motivating examples for our work; it also appears in other programming-language contexts. Specifically, we introduce a simple, general extension of the pi calculus with value passing, primitive functions, and equations among terms. We develop semantics and proof techniques for this extended language and apply them in reasoning about some security protocols.

No abstract

Software Transactional Memory (STM) is an attractive basis for the development of language features for concurrent programming. However, the semantics of these features can be delicate and problematic. In this paper we explore the tradeoffs between semantic simplicity, the viability of efficient implementation strategies, and the flexibility of language constructs. Specifically, we develop semantics and type systems for the constructs of the Automatic Mutual Exclusion (AME) programming model; our results apply also to other constructs, such as atomic blocks. With this semantics as a point of reference, we study several implementation strategies. We model STM systems that use in-place update, optimistic concurrency, lazy conflict detection, and roll-back. These strategies are correct only under non-trivial assumptions that we identify and analyze. One important source of errors is that some efficient implementations create dangerous "zombie" computations where a transaction keeps running after experiencing a conflict; the assumptions confine the effects of these computations.

We describe a design and implementation of security for a distributed system. In our system, applications access security services through a narrow interface. This interface provides a notion of identity that includes simple principals, groups, roles, and delegations. A new operating system component manages principals, credentials, and secure channels. It checks credentials according to the formal rules of a logic of authentication. Our implementation is efficient enough to support a substantial user community.