首页|GPU异构集群的协同计算引擎设计研究

GPU异构集群的协同计算引擎设计研究

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GPU与多核CPU的协同可提高大数据计算效率,然而用户需要同时考虑应用领域内的并行算法逻辑和协同计算过程,为GPU异构协同计算增加了编程难度。所以,在分析GPU异构集群节点之间和节点内协同计算的基础上抽取流程逻辑,提出一个粗细粒度相结合的协同计算引擎,自动生成协同执行计划,用户只需关注应用领域算法的设计和实现。实验表明,该方法与人工编程方案对比发现性能损失控制在 4。2%以内。因此该协同计算引擎能够用于GPU通用计算开发应用中,可有效改善普通用户的开发效率。
COLLABORATIVE COMPUTING ENGINE DESIGN FOR GPU HETEROGENEOUS CLUSTER
The cooperation of GPU and multi-core CPU can improve the efficiency of big data computing.However,users need to consider the parallel algorithm logic and collaborative computing process in the application field at the same time,which increases the programming difficulty for GPU heterogeneous collaborative computing.Therefore,based on the analysis of collaborative computing between and within GPU heterogeneous cluster nodes,the process logic is extracted,and a collaborative computing engine combining coarse and fine granularity is proposed,which automatically generates collaborative execution plans,and users only need to pay attention to the design and implementation of the algorithm in the application field.Experimental results show that the performance loss is less than 4.2%compared with the manual programming scheme.Therefore,the collaborative computing engine can be used in the development and application of GPU general computing,which can effectively improve the development efficiency of ordinary users.

GPU collaborative computingFinite state machineComputing engineTemplate technology

李清清、于欣宁、王海峰

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临沂大学信息科学与工程学院 山东 临沂 276002

GPU协同计算 有限自动机 计算引擎 模板技术

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(12)