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DistME: A Fast and Elastic Distributed Matrix Computation Engine using GPUs
- DistME: A Fast and Elastic Distributed Matrix Computation Engine using GPUs
- Han, Donghyoung; Park, Kyongseok; Nam, Yoon-Min; Kim, Hyunwoo; Lee, Jihye; Kim, Min-Soo
- DGIST Authors
- Kim, Min-Soo
- Issue Date
- 2019 International Conference on Management of Data, SIGMOD 2019, 759-774
- Matrix computation, in particular, matrix multiplication is time-consuming, but essentially and widely used in a large number of applications in science and industry. The existing distributed matrix multiplication methods only focus on either low communication cost (i.e., high performance) with the risk of out of memory or large-scale processing with high communication overhead. We propose a distributed elastic matrix multiplication method called CuboidMM that achieves both high performance and large-scale processing. We also propose a GPU acceleration method that can be combined with CuboidMM. CuboidMM partitions matrices into cuboids for optimizing the network communication cost with considering memory usage per task, and the GPU acceleration method partitions a cuboid into subcuboids for optimizing the PCI-E communication cost with considering GPU memory usage. We implement a fast and elastic matrix computation engine called DistME by integrating CuboidMM with GPU acceleration on top of Apache Spark. Through extensive experiments, we have demonstrated that CuboidMM and DistME significantly outperform the state-of-the-art methods and systems, respectively, in terms of both performance and data size. © 2019 Association for Computing Machinery.
- Association for Computing Machinery
- Related Researcher
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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