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Toward GPU Accelerated Data Stream Processing

Marcus Pinnecke, David Broneske, Gunter Saake
Institute for Technical and Business Information Systems, University of Magdeburg, Germany
27th GI-Workshop on Foundations of Databases, 2015

@article{pinnecke2015toward,

   title={Toward GPU Accelerated Data Stream Processing},

   author={Pinnecke, Marcus and Broneske, David and Saake, Gunter},

   year={2015}

}

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In recent years, the need for continuous processing and analysis of data streams has increased rapidly. To achieve high throughput-rates, stream-applications make use of operator-parallelization, batching-strategies and distribution. Another possibility is to utilize co-processors capabilities per operator. Further, the database community noticed, that a column-oriented architecture is essential for efficient co-processing, since the data transfer overhead is smaller compared to transferring whole tables. However, current systems still rely on a row-wise architecture for stream processing, because it requires data structures for high velocity. In contrast, stream portions are in rest while being bound to a window. With this, we are able to alter the per-window event representation from row to column orientation, which will enable us to exploit GPU acceleration. To provide general-purpose GPU capabilities for stream processing, the varying window sizes lead to challenges. Since very large windows cannot be passed directly to the GPU, we propose to split the variable-length windows into fixed-sized window portions. Further, each such portion has a column-oriented event representation. In this paper, we present a time and space efficient, data corruption free concept for this task. Finally, we identify open research challenges related to co-processing in the context of stream processing.
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