<sc>Springald</sc>: GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data Streams
Pages 1657 - 1671
Abstract
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 article presents <sc>Springald</sc>, an efficient sliding-window operator leveraging GPU devices. <sc>Springald</sc>, incorporated in the <sc>WindFlow</sc> parallel library, processes out-of-order data streams with watermarks propagation. These two features—GPU processing and out-of-orderliness—make <sc>Springald</sc> a novel contribution to this research area. This article describes the methodology behind <sc>Springald</sc>, its design and implementation. We also provide an extensive experimental evaluation to understand the behavior of <sc>Springald</sc> deeply, and we showcase its superior performance against state-of-the-art competitors.
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Springald : GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data Streams
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Published: 01 September 2024
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