CSR: Small: Adaptive Synchronization for Multicore Systems

CSR：小型：多核系统的自适应同步

• 批准号: 1016828
• 负责人: Michael Spear
• 金额: $ 25万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016828&HistoricalAwards=false
• 关键词: CSR; Small; Adaptive; Synchronization; Multicore

Transactional Memory (TM) is among the most promising techniques for simplifying the development of correct parallel programs. Dozens of different techniques have been developed to implement TM, with each appearing ideally suited to some combination of application and hardware characteristics. Unfortunately, when the wrong algorithm is chosen for a workload, pathologically bad performance can result. The problem is particularly acute in programs whose behavior varies overtime: the best TM implementation for one phase of execution may be unacceptable during another phase. This research will create new algorithms for dynamic adaptivity, so that the implementation of TM can be changed repeatedly during program execution, thereby maximizing performance during each program phase.The research will explore both mechanism and policy. It will create new algorithms and heuristics for selecting TM implementations. It will also model the behavior of a wide array of TM algorithms and workloads on multiple architectures, in order to create a knowledge base suitable for guiding adaptivity. To achieve maximum performance, the research will consider a broad set of characteristics, to include TM implementation details, bottlenecks, and overheads; risk of pathology; properties of the hardware environment; and workload requirements such as frequency of inter-thread synchronization, I/O, and nontransactional access to shared data. This research will also develop novel static analyses and a dynamic optimization framework to avoid any overhead while providing robust, adaptive TM. Prototypes and source code will be distributed as open-source software.

事务存储器(Transaction Memory，TM)是简化正确并行程序开发的最有前途的技术之一。已经开发了数十种不同的技术来实现TM，每一种技术似乎都非常适合应用程序和硬件特征的某种组合。不幸的是，当为工作负载选择了错误的算法时，可能会导致病态的性能下降。这个问题在行为超时变化的程序中尤其严重：在执行的一个阶段，最好的TM实现在另一个阶段可能是不可接受的。该研究将创建新的动态自适应算法，使得TM的实现可以在程序执行过程中反复更改，从而最大化每个程序阶段的性能。该研究将从机制和策略两方面进行探索。它将为选择TM实现创建新的算法和启发式方法。它还将对多种架构上广泛的TM算法和工作负载的行为进行建模，以便创建适合于指导适应性的知识库。为了实现最高性能，研究将考虑一组广泛的特征，包括TM实施细节、瓶颈和开销；病理风险；硬件环境的属性；以及工作负载要求，如线程间同步、I/O和对共享数据的非事务性访问的频率。这项研究还将开发新的静态分析和动态优化框架，以避免任何开销，同时提供健壮、自适应的TM。原型和源代码将作为开源软件分发。