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Model-Based Estimation of the Communication Cost of Hybrid Data-Parallel Applications on Heterogeneous Clusters

机译:基于模型的异构集群上混合数据并行应用的通信成本估算

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摘要

Heterogeneous systems composed of CPUs and accelerators sharing communication channels of different performance are getting mainstream in HPC but, at the same time, they show a complexity that makes it difficult to optimize the deployment of a data parallel application. Recent analytical tools such as Functional Performance Models, combined with advanced partitioning algorithms, manage to achieve a balanced configuration by distributing the workload unevenly, according to the performance of the different processing units. Unfortunately, such uneven distribution of the computation load leads to communication unbalances that, very often, render worthless the previous workload balancing efforts. Finding the optimal communication scheme without expensive testing on the executing platform requires an analytical approach to the estimation of the communication cost of different configurations of the application. With this goal in mind, we propose and discuss an extension of the -Lop communication performance model to cover heterogeneous architectures. In order to provide a quantitative assessment of this extended model, we conduct experiments with two representative computational kernels, the SUMMA algorithm and the 2D wave equation solver. The -Lop predictions are compared against the HLogGP model and the observed costs for a variety of configurations, hardware resources and problem sizes.
机译:由CPU和加速器共享的不同性能的通信通道组成的异构系统在HPC中正成为主流,但同时,它们又显示出复杂性,使得难以优化数据并行应用程序的部署。最新的分析工具(例如功能性能模型)与高级分区算法相结合,可以根据不同处理单元的性能,通过不均匀地分配工作负载来实现平衡的配置。不幸的是,计算负载的这种不均匀分布会导致通信不平衡,这常常使以前的工作负载平衡工作变得毫无价值。在执行平台上没有进行昂贵测试的情况下找到最佳通信方案,需要一种分析方法来估算应用程序不同配置的通信成本。考虑到这一目标,我们建议并讨论-Lop通信性能模型的扩展,以涵盖异构体系结构。为了提供对该扩展模型的定量评估,我们使用两个代表性的计算内核SUMMA算法和2D波动方程求解器进行了实验。将-Lop预测与HLogGP模型以及各种配置,硬件资源和问题大小所观察到的成本进行比较。

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