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

这个项目为节能计算定义了一种新的架构，称为FAWN，即“快速微节点阵列”，它使用大量小型、较慢的计算机来完成以前由一台大型、快速计算机处理的任务。通过串联使用更多、更慢的计算机，该架构可以更有效地运行能源。然而，以实际的方式这样做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；计算任务必须在更多的节点上进行拆分，并且负载必须在节点之间进行平衡，这样就不会有一台运行缓慢的计算机导致整个操作延迟太长时间。现代数据中心环境进一步在非常严格的处理延迟要求下运行，以确保用户满意；在FAWN架构上运行时，这些需求可能更难满足。该项目通过开发在快速固态（Flash）存储器上存储和管理大量数据的新技术来解决这些挑战。通过与计算机科学理论专家的合作，该项目正在开发用于内存高效、高度并行的数据分析和数据挖掘的新算法，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，该项目正在使用先进的低功耗处理器构建一个100节点的一汽集群原型。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，并大大降低其能源成本和足迹。它还可以在数据密集型和节能计算的实用算法和系统架构方面大大提高技术水平。

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.