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GPU-Accelerated High-Throughput Online Stream Data Processing
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DOI：10.1109/TBDATA.2016.2616116.png)
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Abstract
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The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of big data. In this paper, we present the design, implementation and evaluation of G-Storm, a GPU-enabled parallel system based on Storm, which harnesses the massively parallel computing power of GPUs for high-throughput online stream data processing. G-Storm has the following desirable features: 1) G-Storm is designed to be a general data processing platform as Storm, which can handle various applications and data types. 2) G-Storm exposes GPUs to Storm applications while preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead data processing with GPUs. 4) G-Storm accelerates data processing further by enabling Direct Data Transfer (DDT), between two executors that process data at a common GPU. We implemented G-Storm based on Storm 0.9.2 and tested it using three different applications, including continuous query, matrix multiplication and image resizing. Extensive experimental results show that 1) Compared to Storm, G-Storm achieves over 7x improvement on throughput for continuous query, while maintaining reasonable average tuple processing time. It also leads to 2.3x and 1.3x throughput improvements on the other two applications, respectively. 2) DDT significantly reduces data processing time.
Keywords:
Stream data processing
GPU
parallel computing
big data infrastructure
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