EAGER: Advanced DAta Movement ANalysis Toolkit (ADAMANT)

EAGER：高级数据移动分析工具包 (ADAMANT)

• 批准号: 1451598
• 负责人: Pietro Cicotti
• 金额: $ 9万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451598&HistoricalAwards=false
• 关键词: EAGER; Advanced; DAta; Movement; ANalysis

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.

今天，很大一部分计算机时间和电力都花在通过计算机的存储子系统移动数据上，而不是执行实际的计算。在科学、大规模和数据密集型计算中尤其如此。大规模计算系统的规模在继续增长，了解这种数据移动的成本对于未来的极端规模系统将是至关重要的，在该系统中，能量和功率将是主要考虑因素，其中存储子系统的复杂性和深度将严重惩罚低效率的数据移动。但是，在大规模科学应用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，为了改善数据移动，从而提高应用程序的效率(包括性能和能力)，需要新的方法来捕获、分析和优化计算机所有层的数据移动-S硬件/软件堆栈。该项目通过研究和开发软件工具来从大规模应用程序中捕获数据移动，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，从而满足了这一需求。这些工具使用二进制分析和工具，这是一种可以自动识别应用程序中的数据对象的技术。通过观察数据对象及其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，以提高大规模应用程序和系统的性能和能效。该项目推动的以数据为中心的观点使模型和方法能够表征和预测未来架构的性能，就像寻求极致规模系统的模型和方法一样。以数据为中心的性能和能效观点与传统的以指令为中心的观点是垂直的。这种新的以数据为中心的观点将启发新的算法和系统，以一种与科学计算社区特别相关的方式共同设计。