Patent
MapReduce multiple programming model based on virtual machine GPU computation
Qin Qian,Yuan Jiabin +1 more
- 04 Sep 2013
11
TL;DR: In this paper, a MapReduce multiple programming model based on virtual machine GPU computation is proposed, where a TaskTracker computation task module is divided into two modules, namely, a CPU calling module and a GPU computation module, and a large-scale data parallel computing task of a fine grit level is completed by a GPU.
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Abstract: The invention discloses a MapReduce multiple programming model based on virtual machine GPU computation. Features of the model are combined with features of a CPU+GPU heterogeneous system structure, a TaskTracker computation task module is divided into two modules, namely, a CPU calling module and a GPU computation module, and a large-scale data parallel computing task of a fine grit level is completed by a GPU. A C/ S mode is adopted by the model in a virtual machine level, a CUDA management terminal is introduced to form a management system, a compute node can be dynamically added to the management system and compute resources can be dynamically stretched out and drawn back in the management system, single task parallel computing on a plurality of GPU cards of a virtual machine is achieved. According to the model, a MapReduce and GPU dual parallel model and a virtualization technology are introduced to a cloud platform, computational efficiency can be effectively improved, and energy consumption is reduced.
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Citations
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TL;DR: In this article, a mapreduce-based multi-GPU cooperative computing method is proposed to simplify the program model and multiplex the existing concurrent codes through a MapReduce program model with cloud computing concurrent computation by combining the structure characteristic of a GPU plus CPU (Central Processing Unit) heterogeneous system, thus reducing the programming complexity, having certain system disaster tolerance capacity and reducing the dependency of equipment.
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Patent
Virtual cluster deployment system and deployment method for high performance computing
Song Wu,Shi Xuanhua,Hai Jin,Fei Wang +3 more
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