Springald: GPU-accelerated Window-based Aggregates over Out-of-Order Data Streams

An increasing number of application domains require high-throughput processing to extract insights from massive data streams. The Data Stream Processing (DSP) paradigm provides formal approaches to analyze structured data streams considered as special, unbounded relations. The most used class of stateful operators in DSP are the ones running sliding-window aggregation, which continuously extracts insights from the most recent portion of the stream. This paper presents Springald, an efficient sliding-window operator leveraging GPU devices. Springald, incorporated in the WindFlow parallel library, processes out-of-order data streams with watermarks propagation. These two features—GPU processing and out-of-orderliness—make Springald a novel contribution to this research area. This paper describes the methodology behind Springald, its design and implementation. We also provide an extensive experimental evaluation to understand the behavior of Springald deeply, and we showcase its superior performance against state-of-the-art competitors.