GPU-Accelerated High-Throughput Online Stream Data Processing - 2018


The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of massive knowledge. During this Project, we tend to gift the look, implementation and analysis of G-Storm, a GPU-enabled parallel system based on Storm, that harnesses the massively parallel computing power of GPUs for top-throughput on-line stream data processing. G-Storm has the following desirable features: one) G-Storm is intended to be a general data processing platform as Storm, that can handle various applications and knowledge sorts. 2) G-Storm exposes GPUs to Storm applications whereas preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead information processing with GPUs. 4) G-Storm accelerates data processing additional by enabling Direct Information Transfer (DDT), between two executors that process knowledge at a typical GPU. We tend to implemented G-Storm based mostly on Storm zero.nine.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 7? improvement on throughput for continuous question, while maintaining affordable average tuple processing time. It also leads to a pair of.three? and one.three? throughput improvements on the opposite two applications, respectively. 2) DDT significantly reduces data processing time.

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