混合CPU与缓存架构对并行HPC及云应用的影响
Effects of Hybrid CPU and Cache Architectures on Parallel HPC and Cloud Applications
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中文总结 AI 辅助
本文研究混合CPU与缓存架构对并行HPC及云应用的影响，揭示线程亲和性、工作负载平衡性对并行应用扩展性的作用，为相关建模优化奠定基础。
中文摘要 AI 辅助
随着英特尔Alderlake架构的发布，混合CPU架构已成为主流桌面计算的一部分。这种向异构CPU架构的转变，对现有并行工作负载产生了多方面的性能和功耗影响。本文研究了混合核心与缓存架构对高度并行HPC工作负载性能的影响，同时阐释了并行工作负载有趣的线程扩展行为，并从定性和定量角度描述了该行为的成因；还探究了混合缓存架构对并行共享数据HPC应用的影响。最后，本文指出两点结论：1）存在工作负载不平衡（即应用中线程执行的工作量不同）的并行应用，在未启用线程亲和性时，跨混合核心的扩展性更好；2）混合缓存架构对并行共享数据应用的影响极小，仅在部分带锁的工作负载中存在差异。本研究为后续工作奠定了基础，后续将扩展该研究以对并行工作负载和混合CPU架构进行建模，进而在执行时间、内存使用和功耗方面提升其性能。
英文摘要
Hybrid CPU architectures have entered the mainstream desktop computing with the announcement of Intel's Alderlake architecture. Such a transition to heterogeneous CPU architecture has various performance and power implications on existing parallel workloads. In this paper we study the effects and impact of hybrid core and cache architecture on the performance of highly parallel HPC workloads. We also illustrate interesting thread scaling behavior for parallel workloads and describes the reason for such behavior both qualitatively and quantitatively. We also explore the impact of hybrid cache architecture on parallel shared data HPC applications. Finally, we illustrate that 1) parallel applications with work imbalance (i.e., threads in application perform different amount of work) scale better across hybrid cores when thread affinity is disabled and 2) hybrid cache architecture has very little impact on parallel shared data applications except for some workloads with locks. This work lays the foundation for our future work which focuses on extending this work to model parallel workloads and hybrid CPU architectures to improve their performance in terms of execution time, memory usage and power consumption.
