DC: Medium: Designing and Programming a Low-Power Cluster Architecture for Data-Intensive Workloads

该项目定义了一种新的高能效计算体系结构，称为FOWN，或“快速Wimpy节点阵列”，它使用大量小型、速度较慢的计算机来执行以前由一台大型、快速的计算机处理的任务。通过同时使用更多、更慢的计算机，该架构可以更高效地运行能源。然而，要以实际的方式做到这一点，需要克服许多重大挑战：单个节点的内存比程序员习惯的要少得多；计算任务必须分配到更多数量的节点上，并且必须在它们之间平衡负载，以便不会有一台速度较慢的计算机导致整体操作延迟太长。为了确保用户满意度，现代数据中心环境对处理延迟的要求更加严格；在小鹿架构上运行时，这些要求可能更难满足。该项目正在开发在快速固态(闪存)内存上存储和管理海量数据的新技术，以应对这些挑战。通过与CS理论专家的合作，该项目正在为内存高效、高度并行的数据分析和数据挖掘开发新的算法，并在要求苛刻的高性能环境中实施和评估这些算法。通过与英特尔的合作，该项目正在使用尖端的低功耗处理器构建一个100节点的小鹿集群原型。该项目有可能大幅降低大型互联网服务的资本和运营成本，并大幅降低它们的能源成本和占地面积。它还可以大大提高用于数据密集型和能效计算的实用算法和系统架构的技术水平。

This project defines a novel architecture for energy efficient computing, called FAWN, or a "Fast Array of Wimpy Nodes", which uses a large collection of small, slower computers to do tasks formerly handled by a single large, fast computer. By using more, slower, computers in tandem, the architecture runs more energy efficiently. However, doing so in a practical manner requires overcoming many significant challenges: The individual nodes have much less memory than programmers are used to; the computational task must be split across a much larger number of nodes, and the load must be balanced between them so that no single slow computer will cause the overall operation to be delayed too long. Modern datacenter environments further operate under very strict requirements for processing delay in order to ensure user satisfaction; these requirements may be harder to meet when running on the FAWN architecture.The project is tackling these challenges by developing new techniques for storing and managing large volumes of data on fast solid-state(Flash) memory. Through collaboration with experts in CS theory, the project is developing new algorithms for memory-efficient, highly parallel data analysis and data-mining, and implementing and evaluating them in a demanding, high-performance environment. Through a collaboration with Intel, the project is constructing a 100-node prototype of a FAWN cluster using cutting-edge low-power processors.This project has the potential to substantially reduce the capital and operating costs of large Internet services, and to greatly reduce their energy costs and footprint. It may also substantially advance the state of the art in practical algorithms and systems architectures for data-intensive and energy-efficient computing.