Data movement between the CPU and main memory is a first-order obstacle against improving 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 hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. 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 to more memory-centric techniques, 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 data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.

中文翻译：

---

DAMOV：评估数据移动瓶颈的新方法论和基准套件

CPU和主内存之间的数据移动是现代系统中提高性能，可伸缩性和能源效率的首要障碍。计算机系统采用了多种技术来减少与数据移动相关的开销，其范围从传统机制（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）），其中一些计算是移到内存附近。我们的目标是有条不紊地确定广泛应用程序中潜在的数据移动源，并将传统的以计算为中心的数据移动缓解技术与以内存为中心的技术进行全面比较，从而对缓解每个源的最佳技术有严格的了解。数据移动。有了这个目标，我们在广泛的应用程序领域中对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/移出主存储器的基本程序属性。我们开发了第一种系统的方法，根据造成数据移动瓶颈的来源对应用程序进行分类。从我们在345个应用程序中对77K函数的大规模表征中，我们选择144个函数形成了第一个用于主内存数据移动研究的开源基准测试套件（DAMOV）。我们选择了多种功能，这些功能（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NDP为例 我们找出有关不同数据移动瓶颈的新见解，并使用这些见解来确定特定应用程序最合适的数据移动缓解机制。我们在https://github.com/CMU-SAFARI/DAMOV上开放了DAMOV的源代码以及我们新的表征方法的完整源代码。