Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres

我们的世界正处于一场“大数据”革命中，人们无处不在地收集、分析和查询前所未有的种类和规模的数据集。管理这些数据集所需的巨大存储量和处理能力已导致从桌面处理过渡到数据中心内的仓库级计算。谷歌和Facebook等公司使用的最先进的数据中心消耗的电力为20至30兆瓦，相当于2万个家庭，这些公司每家都需要许多数据中心。全球数据中心的能源足迹估计约占全球能源消耗的2%，并且每五年翻一番[33，34]。当代数据中心的平均管理费用为90%[32]，这意味着它们提供1兆瓦的IT支持需要消耗高达1.9兆瓦的电力；这既不划算，也不环保。如果数据的指数级增长和处理能力要以公众和行业都依赖的方式扩大规模，我们就必须解决数据中心能源危机，否则就会面临进展停滞的现实。随着半导体行业无法进一步降低处理器和内存芯片的工作电压，挑战在于开发以能源为第一级设计约束的大规模以数据为中心的计算技术。红利项目通过垂直整合、专业化和跨层优化来攻击数据中心的能效瓶颈。我们的愿景是将结合了CPU、GPU和特定于任务的加速器的异类数据中心作为统一实体呈现给应用程序开发人员，并让运行时在任务执行期间优化系统资源的利用。红利采用了异构性，通过广泛的硬件专门化显著降低了每项任务的能耗，同时保持了同构架构的可编程性。为了降低通信延迟和能耗，Divide利用了SoC集成，并且更喜欢精简的点对点消息传递结构，而不是复杂的面向连接的网络协议。红利通过调整和扩展业界领先的异类系统架构、编程语言和运行时计划来应对可编程性挑战，以应对能源感知和数据移动。红利通过一组API提供跨层能源优化框架，这些API用于硬件、编译、运行时和应用程序层之间的能源核算和反馈。红利项目将迎来一类新的垂直整合的数据中心，并将通过将数据中心的用电效率提高至少50%来第一次尝试解决能源危机。

Our world is in the midst of a "big data" revolution, driven by the ubiquitous ability to gather, analyse, and query datasets of unprecedented variety and size. The sheer storage volume and processing capacity required to manage these datasets has resulted in a transition away from desktop processing and toward warehouse-scale computing inside data centres. State-of-the-art data centres, employed by the likes of Google and Facebook, draw 20-30 MW of power, equivalent to 20,000 homes, with these companies needing many data centres each. The global data centre energy footprint is estimated at around 2% of the world's energy consumption and doubles every five years [33, 34]. Contemporary data centres have an average overhead of 90% [32], meaning that they consume up to 1.9 MW to deliver 1 MW of IT support; this is not cost-effective or environmentally sound. If the exponential data growth and processing capacity are to scale in the way that both the public and industry have come to rely upon, we must tackle the data centre energy crisis or face the reality of stagnated progress. With the semiconductor industry's inability to further lower operating voltages in processor and memory chips, the challenge is in developing technologies for large-scale data-centric computation with energy as a first-order design constraint.The DIVIDEND project attacks the data centre energy efficiency bottleneck through vertical integration, specialisation, and cross-layer optimisation. Our vision is to present heterogeneous data centres, combining CPUs, GPUs, and task-specific accelerators, as a unified entity to the application developer and let the runtime optimise the utilisation of the system resources during task execution. DIVIDEND embraces heterogeneity to dramatically lower the energy per task through extensive hardware specialisation while maintaining the ease of programmability of a homogeneous architecture. To lower communication latency and energy, DIVIDEND leverages SoC integration and prefers a lean point-to-point messaging fabric over complex connection-oriented network protocols. DIVIDEND addresses the programmability challenge by adapting and extending the industry-led heterogeneous systems architecture programming language and runtime initiative to account for energy awareness and data movement. DIVIDEND provides for a cross-layer energy optimisation framework via a set of APIs for energy accounting and feedback between hardware, compilation, runtime, and application layers. The DIVIDEND project will usher in a new class of vertically integrated data centres and will take a first stab at resolving the energy crisis by improving the power usage effectiveness of data centres by at least 50%.