SHF: AF: Small: Locality with Dynamic Parallelism

SHF：AF：小：具有动态并行性的局部性

• 批准号: 1018188
• 负责人: Guy Blelloch
• 金额: $ 44.91万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018188&HistoricalAwards=false
• 关键词: SHF; AF; Small; Locality; Dynamic

With the recent dominance of computers with many parallel cores, and the widespread use of large data centers, the need to supply high-level, simple and general approaches to developing parallel codes for these machines has become critical. There are many challenges to effectively developing such parallel codes, but certainly a principle difficulty is dealing with communication costs - or when looked at from the other side, taking advantage of locality. Unfortunately this challenge has only become more difficult on modern parallel machines that have many forms of locality - network latency and bandwidth, shared and distributed caches, partitioned memories, and secondary storage in a variety of organizations.To address this problem this project is developing an approach for programmers to understand locality in their parallel code without needing to know any details of the particular parallel machine they are using. In the approach programmers express the full dynamic parallelism of their algorithm without describing how it is mapped onto processors, and are given a simple high-level model for analyzing locality. The research is based on the conjecture that locality should be viewed by the programmer as a property of the algorithm or code and not the machine. To ensure that real machines can take proper advantage of the locality analyzed in the model, the research is developing scheduling approaches that map the "algorithm locality" onto various forms of machine locality, including shared caches, distributed caches, trees of caches, and distributed memory machines. The results of the research include both theoretical bounds for such schedulers on specific machine organizations, and experimental validation on a set of benchmark applications.

随着具有许多并行核的计算机的最近主导地位，以及大型数据中心的广泛使用，为这些机器提供高级、简单和通用的方法来开发并行代码的需求变得至关重要。 有效地开发这种并行代码存在许多挑战，但肯定的是，一个主要的困难是处理通信成本-或者从另一方面看，利用本地化。 不幸的是，这种挑战在现代并行机上变得更加困难，这些并行机具有许多形式的局部性-网络延迟和带宽，共享和分布式缓存，分区存储器，为了解决这个问题，这个项目正在开发一种方法，让程序员能够理解他们并行代码中的局部性，而不需要知道他们所在的特定并行机的任何细节。使用. 在这种方法中，程序员表达了他们的算法的完全动态并行性，而不描述它是如何映射到处理器上的，并给出了一个简单的高级模型来分析局部性。 这项研究是基于这样一种猜想，即程序员应该将局部性视为算法或代码的属性，而不是机器的属性。 为了确保真实的机器可以适当地利用模型中分析的局部性，该研究正在开发调度方法，该方法将“算法局部性”映射到各种形式的机器局部性，包括共享缓存、分布式缓存、缓存树和分布式内存机器。研究的结果包括理论上的界限，这样的机器组织，和一组基准应用程序的实验验证。