cuSTINGER: Supporting dynamic graph algorithms for GPUs
cuSTINGER: Supporting dynamic graph algorithms for GPUs
复制标题
cuSTINGER：支持 GPU 的动态图算法
DOI：
10.1109/hpec.2016.7761622
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
J. Szwarcfiter
中科院分区：
文献类型：
--
作者：
E. Cáceres;F. Dehne;H. Mongelli;S. W. Song;J. Szwarcfiter
custinger是一种针对NVIDIA GPU的新图形数据结构，专为随时间推移而演变的流式图形而设计。custinger使算法设计人员能够更高的生产力和效率来实施基于GPU的分析，减轻程序员管理内存和数据放置的负担。与静态图形数据结构相比，静态图形数据结构可能需要在设备和主机存储器之间来回传输整个图形以进行每次更新或需要在设备上重建，custinger只需要传输更新本身;减少了传输的总数据量。custinger为用户提供了灵活性，根据应用程序的需要，一次更新一条边或通过批量更新来更新图形。custinger支持极高的更新速率，对于10k更新的中型批量，每秒超过100万次更新，对于数百万次更新的大型批量，每秒超过1000万次更新。
cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed for 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 forth between the device and the host memories 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 10k updates and 10 million updates per second for large batches with millions of updates.