EAGER: Advanced DAta Movement ANalysis Toolkit (ADAMANT)

今天，计算机时间和功率的很大一部分花在通过计算机的内存子系统移动数据上，而不是用于执行实际的计算。在科学、大规模和数据密集型计算中尤其如此。大规模计算系统在规模上持续增长，了解这种数据移动的成本对未来的极端规模系统至关重要，在这种系统中，能源和功率将是主要问题，内存子系统的复杂性和深度将严重惩罚低效的数据移动。但是，在大规模科学应用程序中捕获和理解数据移动的细节可能是一个具有挑战性的问题。因此，为了改进数据移动，从而提高应用程序的效率（包括性能和功率），需要新的方法来捕获、分析和优化跨计算机所有层的数据移动。S硬件/软件栈。该项目通过研究和开发软件工具来解决这一需求，这些软件工具可以捕获来自大规模应用程序的数据移动，然后通过数据对象的特征将信息连接到程序，从而以易于分析和理解的方式呈现这些信息。这些工具使用二进制分析和仪器，这是一种可以自动识别应用程序中的数据对象的技术。通过观察数据对象、它们的访问模式和数据移动信息，科学家和应用程序开发人员可以分析和优化他们的应用程序，以提高大型应用程序和系统的性能和能效。这个项目所提倡的以数据为中心的观点使模型和方法能够描述和预测未来体系结构的性能，就像那些寻求极端规模系统的模型和方法一样。以数据为中心的性能和能源效率视图与传统的以指令为中心的视图是正交的。这种以数据为中心的新观点将激发新的算法和系统，以一种与科学计算社区特别相关的方式共同设计。

Today, a significant fraction of computer time and power is spent moving data through the memory sub-system of the computer rather than performing the actual computation. This is especially true in scientific, large-scale, and data-intensive computations. Large-scale computing systems are continuing to grow in size and understanding the cost of this data movement will be of fundamental importance on future Extreme scale system, in which energy and power will be primary concerns, and where the complexity and depth of the memory sub-system will heavily penalize inefficient data movement. But capturing and understanding the details of data movement in a large-scale scientific application can be a challenging problem. Therefore to improve data movement and hence improve applications efficiency (both performance and power), new methodologies are needed to capture, analyze, and optimize data-movement across all the layers of the computer?s hardware/software stack.This project addresses this need by researching and developing software tools to capture data movement from large-scale applications, and then to present this information in a manner that is easy to analyze and understand by connecting the information to the program via a characterization of data objects. The tools use binary analysis and instrumentation, a technique that can automate the identification of data objects within an application. By observing data objects, their access patterns, and data movement information, scientists and application developers can analyze and optimize their applications to increase performance and energy efficiency of large-scale applications and systems.The data-centric view promoted by this project enables models and methodologies for characterizing and predicting performance on future architectures, as those sought for extreme scale systems. The data-centric view of performance and energy efficiency is orthogonal to the traditional instructions-centric view. This new data-centric view will inspire new algorithms and systems, co-designed in a way that is particularly relevant to the scientific computing community.