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How Pre-multicore Methods and Algorithms Perform in Multicore Era

机译：预多核方法和算法在多核时代的表现如何

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Many classical methods and algorithms developed when single-core CPUs dominated the parallel computing landscape, are still widely used in the changed multicore world. Two prominent examples are load balancing, which has been one of the main techniques for minimization of the computation time of parallel applications since the beginning of parallel computing, and model-based power/energy measurement techniques using performance events. In this paper, we show that in the multicore era, load balancing is no longer synonymous to optimization and present recent methods and algorithms for optimization of parallel applications for performance and energy on modern HPC platforms, which do not rely on load balancing and often return imbalanced but optimal solutions. We also show that some fundamental assumptions about performance events, which have to be true for the model-based power/energy measurement tools to be accurate, are increasingly difficult to satisfy as the number of CPU cores increases. Therefore, energy-aware computing methods relying on these tools will be increasingly difficult to verify.
机译：当单核CPU主导并行计算领域时，开发出许多经典方法和算法，但仍在变化多核世界中广泛使用。自负载计算开始以来，负载平衡一直是使并行应用程序的计算时间最小化的主要技术之一，而负载平衡是其中一项主要的技术；而使用性能事件的基于模型的功率/能量测量技术则是两个突出的示例。本文表明，在多核时代，负载平衡不再是优化的代名词，并且介绍了现代方法和算法，这些方法和算法可在现代HPC平台上优化性能和能耗的并行应用程序，而这些方法和算法不依赖于负载平衡并且经常返回不平衡但最佳的解决方案。我们还表明，随着CPU内核数量的增加，对于满足性能事件的一些基本假设（对于基于模型的功率/能量测量工具来说必须是正确的）才是正确的，这些假设越来越难以满足。因此，依赖于这些工具的能量感知计算方法将越来越难以验证。

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