Heterogeneous cluster systems consisting of CPUs and different kinds of accelerators have become mainstream in HPC. Programming such systems is a difficult task and requires addressing manifold challenges that stem from the intricate composition of such systems and peculiarities of scientific applications. A broad range of obstacles preventing efficient execution have to be considered and dealt with properly. In this paper, we propose a systematic approach and a framework that is capable of providing comprehensive support for running data-parallel applications in heterogeneous asymmetric clusters. Our implementation provides work partitioning and distribution by ensuring workload balance in the cluster while handling of partitioning-induced communication and synchronization in a transparent way. In our experimental section, we choose 11 representative scientific applications from different domains to evaluate our approach. Experimental results show a strong speedup and workload balance for different cluster configurations.

中文翻译：

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clusterCL：全面支持异构非对称集群中的多内核数据并行应用

由 CPU 和不同类型的加速器组成的异构集群系统已成为 HPC 的主流。对此类系统进行编程是一项艰巨的任务，需要解决源于此类系统的复杂组合和科学应用特性的多种挑战。必须考虑和妥善处理各种阻碍有效执行的障碍。在本文中，我们提出了一种能够为在异构非对称集群中运行数据并行应用程序提供全面支持的系统方法和框架。我们的实现通过确保集群中的工作负载平衡来提供工作分区和分配，同时以透明的方式处理分区引起的通信和同步。在我们的实验部分，我们从不同领域选择了 11 个具有代表性的科学应用来评估我们的方法。实验结果表明，不同的集群配置有很强的加速和工作负载平衡。