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DSP-CC: I/O efficient parallel computation of connected components in billion-scale networks
- DSP-CC: I/O efficient parallel computation of connected components in billion-scale networks
- Kim, Min-Soo; Lee, Sangyeon; Han, Wook-Shin; Park, Himchan; Lee, Jeong-Hoon
- DGIST Authors
- Kim, Min-Soo; Park, Himchan
- Issue Date
- 32nd IEEE International Conference on Data Engineering, ICDE 2016, 1504-1505
- Article Type
- Conference Paper
- Computing connected components (CC) is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single machine, there have been proposed a number of distributed graph processing methods. The representative ones for CC are Hash-To-Min and PowerGraph. Hash-To-Min focuses on minimizing the number of MapReduce rounds, but is still slower than in-memory methods, PowerGraph is a fast and general in-memory graph method, but requires a lot of machines for handling billion-scale graphs. We propose an ultra-fast parallel method DSP-CC, using only a single PC that exploits secondary storage like a PCI-E SSD for handling billion-scale graphs. It can compute connected components I/O efficiently using only a limited size of memory. Our experimental results show that DSP-CC significantly outperforms the representative methods including Hash-To-Min and PowerGraph. © 2016 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Kim, Min Soo
Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
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- Department of Information and Communication EngineeringInfoLab2. Conference Papers
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