Data movement between the CPU and main memory is a first-order obstacle against improv ing performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarch ies, aggressive hardware prefetcher s) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques (e.g., cach ing and prefetch ing) to more memory-centric techniques (e.g., NDP), thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different dat...

中文翻译：

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考虑插电式电动汽车对不平衡配电网高渗透率的解决VVO问题的两阶段优化策略


CPU 和主内存之间的数据移动是提高现代系统性能、可扩展性和能源效率的首要障碍。计算机系统采用一系列技术来减少与数据移动相关的开销，从传统机制（例如，深层多级缓存层次结构、积极的硬件预取器）到新兴技术，例如近数据处理（NDP），其中一些计算移至内存附近。之前的 NDP 工作使用不同的分析方法和工具来调查数据移动瓶颈的根本原因。然而，对于可以识别不同数据移动瓶颈的关键指标及其与传统和新兴数据移动缓解机制的关系仍然缺乏了解。我们的目标是系统地识别广泛应用程序中数据移动的潜在来源，并全面比较传统的以计算为中心的数据移动缓解技术（例如，缓存和预取）与更多以内存为中心的技术（例如，NDP），从而对减轻每个数据移动来源的最佳技术有严格的了解。考虑到这一目标，我们对跨广泛应用领域的各种应用程序进行了首次大规模表征，以识别导致数据移入/移出主内存的基本程序属性。我们开发了第一个系统方法，根据造成数据移动瓶颈的来源对应用程序进行分类。从我们对 345 个应用程序的 77K 函数的大规模表征中，我们选择了 144 个函数来构成第一个用于主内存数据移动研究的开源基准套件 (DAMOV)。 我们选择了各种各样的函数，这些函数（1）代表不同类型的数据移动瓶颈，（2）来自广泛的应用领域。以新民主党为案例研究，我们发现了关于不同数据的新见解......