cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs

被引:0
|
作者
Green, Oded [1 ]
Bader, David A. [1 ]
机构
[1] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA
来源
2016 IEEE HIGH PERFORMANCE EXTREME COMPUTING CONFERENCE (HPEC) | 2016年
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed tor streaming graphs that evolve over time. cuSTINGER enables algorithm designers greater productivity and efficiency for implementing GPU-based analytics, relieving programmers of managing memory and data placement. In comparison with static graph data structures, which may require transferring the entire graph back and torth between the device and the host memo ries for each update or require reconstruction on the device, cuSTINGER only requires transferring the updates themselves; reducing the total amount of data transferred. cuSTINGER gives users the flexibility, based on application needs, to update the graph one edge at a time or through batch updates. cuSTINGER supports extremely high update rates, over 1 million updates per second for mid-size batched with lOk updates and 10 million updates per second tor large batches with millions of updates.
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页数:6
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