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

Reliable scientific service compositions

Abstract. Distributed service oriented architectures (SOAs) are increas-ingly used by users, who are insufficiently skilled in the art of distributed system programming. A good example are computational scientists who build large-scale distributed systems using service-oriented Grid comput-ing infrastructures. Computational scientists use these infrastructure to build scientific applications, which are composed from basic Web ser-vices into larger orchestrations using workflow languages, such as the Business Process Execution Language. For these users reliability of the infrastructure is of significant importance and that has to be provided in the presence of hardware or operational failures. The primitives avail-able to achieve such reliability currently leave much to be desired by users who do not necessarily have a strong education in distributed sys-tem construction. We characterise scientific service compositions and the environment they operate in by introducing the notion of global scien-tific BPEL workflows. We outline the threats to the reliability of such workflows and discuss the limited support that available specifications and mechanisms provide to achieve reliability. Furthermore, we propose a line of research to address the identified issues by investigating auto-nomic mechanisms that assist computational scientists in building, exe-cuting and maintaining reliable workflows.