A Calculus for Orchestration of Web Services

We introduce COWS (Calculus for Orchestration of Web Services), a new foundational language for SOC whose design has been influenced by WS-BPEL, the de facto standard language for orchestration of web services. COWS combines in an original way a number of ingredients borrowed from well-known process calculi, e.g. asynchronous communication, polyadic synchronization, pattern matching, protection, delimited receiving and killing activities, while resulting different from any of them. Several examples illustrates COWS peculiarities and show its expressiveness both for modelling imperative and orchestration constructs, e.g. web services, flow graphs, fault and compensation handlers, and for encoding other process and orchestration languages

Dynamic Control Flow in Large-Scale Machine Learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure

Computation of Buffer Capacities for Throughput Constrained and Data Dependent Inter-Task Communication

Streaming applications are often implemented as task graphs. Currently, techniques exist to derive buffer capacities that guarantee satisfaction of a throughput constraint for task graphs in which the inter-task communication is data-independent, i.e. the amount of data produced and consumed is independent of the data values in the processed stream. This paper presents a technique to compute buffer capacities that satisfy a throughput constraint for task graphs with data dependent inter-task communication, given that the task graph is a chain. We demonstrate the applicability of the approach by computing buffer capacities for an MP3 playback application, of which the MP3 decoder has a variable consumption rate. We are not aware of alternative approaches to compute buffer capacities that guarantee satisfaction of the throughput constraint for this application

The Calculus of Signal Flow Diagrams I: Linear relations on streams

We introduce a graphical syntax for signal flow diagrams based on the language of symmetric monoidal categories. Using universal categorical constructions, we provide a stream semantics and a sound and complete axiomatisation. A certain class of diagrams captures the orthodox notion of signal flow graph used in control theory; we show that any diagram of our syntax can be realised, via rewriting in the equational theory, as a signal flow graph