基于DTPS算法的异构集群优化策略
发布时间：2018-10-14 17:43
【摘要】：随着高性能计算机的发展,一种基于CPU-GPU的异构集群逐渐被人们所关注。相比于传统集群,它更经济环保,且拥有更高的运算速度。但异构模式下效率较低的短板也限制着异构集群的发展。本文提出的DTPS算法,通过动态调整异构集群下CPU与GPU任务划分的比例,整合集群计算资源,使集群的计算效率达到相对较高的水平,并通过实验证明了算法的有效性。
[Abstract]:With the development of high performance computer, a heterogeneous cluster based on CPU-GPU has been paid more and more attention. Compared with the traditional cluster, it is more economical and environmentally friendly, and has higher computing speed. However, the development of heterogeneous cluster is limited by the low efficiency board in heterogeneous mode. The DTPS algorithm proposed in this paper dynamically adjusts the ratio of CPU and GPU task partition in heterogeneous cluster, integrates the computing resources of cluster, and makes the computing efficiency of cluster reach a relatively high level. The experimental results show that the algorithm is effective.
【作者单位】： 安徽大学计算机科学与技术学院;
【分类号】：TP38
，
本文编号：2271161
[Abstract]:With the development of high performance computer, a heterogeneous cluster based on CPU-GPU has been paid more and more attention. Compared with the traditional cluster, it is more economical and environmentally friendly, and has higher computing speed. However, the development of heterogeneous cluster is limited by the low efficiency board in heterogeneous mode. The DTPS algorithm proposed in this paper dynamically adjusts the ratio of CPU and GPU task partition in heterogeneous cluster, integrates the computing resources of cluster, and makes the computing efficiency of cluster reach a relatively high level. The experimental results show that the algorithm is effective.
【作者单位】： 安徽大学计算机科学与技术学院;
【分类号】：TP38
，
本文编号：2271161
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