Abstract
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. We implemented G-Storm based on Storm 0.9.2 and tested it using two different applications: continuous query and matrix multiplication. Extensive experimental results show that 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 throughput improvement for the matrix multiplication application.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 307-312 |
| Number of pages | 6 |
| ISBN (Print) | 9781479999255 |
| DOIs | |
| State | Published - Dec 22 2015 |
| Event | 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: Oct 29 2015 → Nov 1 2015 |
Other
| Other | 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 |
|---|---|
| Country | United States |
| City | Santa Clara |
| Period | 10/29/15 → 11/1/15 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Software