A MapReduce Computing Framework Based on GPU Cluster

In recent years, GPU has become a power-efficient device for high performance computing and is widely used in highly parallel application. Its hierarchy of threads and memory has been proven successful for large scale multithread applications. However, how to efficiently program on GPU so as to full...

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Vydáno v:2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing s. 1902 - 1907
Hlavní autoři: Heng Gao, Jie Tang, Gangshan Wu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2013
Témata:
Data processing
GPU Cluster
Graphics processing units
High performance computing
Load Balancing
Load management
MapReduce
Mars
Parallel processing
Programming
Seismic migration
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Shrnutí:In recent years, GPU has become a power-efficient device for high performance computing and is widely used in highly parallel application. Its hierarchy of threads and memory has been proven successful for large scale multithread applications. However, how to efficiently program on GPU so as to fully utilize the computing power of GPUs is still a main problem for those potential users. We designed and implemented a new parallel GPU programming framework based on MapReduce. In our framework, a distributed file system (GlusterFS) was employed to store data distributely. The aim of the framework is to improve the efficiency, transparence and scalability of high performance computing on GPU clusters. The dynamic load balancing was taken into consideration more specifically. How typical tasks in oil industry are modified to fit into the framework was demonstrated. Prestack Kirchhoff time migration (PKTM) of seismic data was tested which achieved good acceleration performance.
DOI:10.1109/HPCC.and.EUC.2013.273