DataFlow Systems: From Their Origins to Future Applications in Data Analytics, Deep Learning, and the Internet of Things

Abstract

With the slowdowns in Dennard scaling and limited performance gain in multi-core scaling, we are witnesses of the high-performance computing shift to domain-specific hardware systems which empower big data and high-performance applications. Likewise, dataflow systems are experiencing a revival with both hardware and software approaches widely exploited. In our work, we give an overview of dataflow system origins and similar technologies such as systolic architecture whose principles are applied by some of today’s leading high-performance systems such as Multiscale dataflow Computing (MDC). In the second part, we highlight certain applications that could benefit from delegating critical processing to a MDC system. We emphasize algorithms and applications from data analytics, deep learning, and the Internet of Things (IoT), with a special focus on their execution within the cloud environment. We discuss the integration of software distributed dataflow systems such as Apache Spark with MDC systems, analyze design issues and challenges for implementation of deep neural networks using MDC, and how semantic-enabled IoT platforms and services could be improved by using MDC systems in order to become more effective. We expect that these selected case studies would motivate researchers to investigate engagement of hardware dataflow systems to support applications from other areas with similarly rigid requirements.