《计算机应用研究》|Application Research of Computers

面向大数据复杂应用的GPU协同计算模型

GPU collaborative computing model for complex applications in large-scale data processing

免费全文下载 (已被下载 次)  
获取PDF全文
作者 张龙翔,曹云鹏,王海峰
机构 1.临沂大学 信息科学与工程学院,山东 临沂 276002;2.山东省网络重点实验室临沂大学研究所,山东 临沂 276002
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2020)07-026-2049-05
DOI 10.19734/j.issn.1001-3695.2019.02.0016
摘要 大数据计算中存在流计算、内存计算、批计算和图计算等不同模式,各种计算模式有不同的访存、通信和资源利用等特征。GPU异构集群在大数据分析处理中得到广泛应用,然而缺少研究GPU异构集群在大数据分析中的计算模型。多核CPU与GPU协同计算时不仅增加了计算资源的密度,而且提高节点间和节点内的通信复杂度。为了从理论上研究GPU与多核CPU协同计算问题,面向多种计算模式建立一个多阶段的协同计算模型(p-DCOT)。p-DCOT以BSP大同步并行模型为核心,将协同计算过程分成数据层、计算层和通信层三个层次,并且延用DOT模型的矩阵来形式化描述计算和通信行为。通过扩展p-DOT模型描述节点内和节点间的协同计算行为,细化了负载均衡的参数并证明时间成本函数,最后用典型计算作业验证模型及参数分析的有效性。该协同计算模型可成为揭示大数据分析处理中协同计算行为的工具。
关键词 协同计算模型; 计算模式; 大数据处理; GPU异构集群
基金项目 山东省自然科学基金面上项目(ZR2017MF050)
山东省高等学校科学技术计划项目(J17KA049)
山东省重点研发项目(2018GGX101005,2017CXGC0701,2016GGX109001)
本文URL http://www.arocmag.com/article/01-2020-07-026.html
英文标题 GPU collaborative computing model for complex applications in large-scale data processing
作者英文名 Zhang Longxiang, Cao Yunpeng, Wang Haifeng
机构英文名 1.Information Science & Engineering School,LinYi University,Linyi Shandong 276002,China;2.Linda Institute,Shandong Provincial Key Laboratory of Network based Intelligent Computing,Linyi Shandong 276002,China
英文摘要 The large-scale data computig process includes different modes which are streaming computing, memory computing, batching computing and graph computing mode. Each mode has different access memory, communication and resource utilization. GPU clusters are widely used in large-scale data processing. However, there is lack of computing model for GPU cluster in large-scale data process. The collaborative computing between GPU and CPU not only increases density of computing resources but also improves communication complexity between inter-nodes and intra-nodes. To explore rule of collaborative computing process, this paper built a novel multi-stage collaborative computing model(p-DCOT) for multi-computing modes. This model was based on BSP model, which was large synchronous parallel model. It divided the collaborative computing process into three levels: data layer, computing layer and communication layer. This model also used matrix of DOT model to describe computing and communication behaviors. Then it extended the p-DOT model to describe collaborative computing behavior within computing nodes and refined the parameters of workloads balancie. This model could prove the time cost function of collaborative computing model. Finally, the typical computing tasks verified the model validity and parameter analysis. This collaborative computing model will be a tool to reveal collaborative computing behaviors in large-scale data analysis processing.
英文关键词 collaborative computing model; computing mode; large-scale data processing applications; GPU heterogeneous cluster
参考文献 查看稿件参考文献
 
收稿日期 2019/2/11
修回日期 2019/3/27
页码 2049-2053
中图分类号 TP393
文献标志码 A