Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres

分布式异构垂直集成节能数据中心

• 批准号: EP/M015823/1
• 负责人: Michael O'Boyle
• 金额: $ 18.24万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM015823%2F1
• 关键词: Distributed; Heterogeneous; Vertically; IntegrateD; ENergy

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%.

我们的世界正处于一场“大数据”革命之中，这场革命的驱动力是无处不在的收集、分析和查询前所未有的多样性和规模的数据集的能力。管理这些数据集所需的庞大存储容量和处理能力导致了从桌面处理向数据中心内部仓库规模计算的过渡。谷歌和Facebook等公司采用的最先进的数据中心消耗20-30兆瓦的电力，相当于20，000个家庭，这些公司每个都需要许多数据中心。全球数据中心的能源足迹估计约占世界能源消耗的2%，每五年翻一番[33，34]。现代数据中心的平均开销为90% [32]，这意味着它们消耗高达1.9兆瓦的IT支持，这既不符合成本效益，也不环保。如果数据的指数增长和处理能力要以公众和行业所依赖的方式扩展，我们必须解决数据中心的能源危机，否则就必须面对停滞不前的现实。由于半导体行业无法进一步降低处理器和存储器芯片的工作电压，开发以能源为一阶设计约束的大规模数据中心计算技术面临挑战。DIVIDEND项目通过垂直整合、专业化和跨层优化解决数据中心能效瓶颈。我们的愿景是将异构数据中心，结合CPU，GPU和特定任务的加速器，作为一个统一的实体提供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。DIVIDEND支持异构性，通过广泛的硬件专业化大大降低每个任务的能耗，同时保持同构架构的易编程性。为了降低通信延迟和能耗，DIVIDEND利用SoC集成，并优先采用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。DIVIDEND通过调整和扩展行业领先的异构系统架构编程语言和运行时计划来解决可编程性挑战，以解决能源意识和数据移动问题。DIVIDEND通过一组API提供跨层能源优化框架，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。DIVIDEND项目将引入一种新的垂直整合数据中心，并将通过将数据中心的电力使用效率提高至少50%来解决能源危机。

项目成果

Function Merging by Sequence Alignment

• DOI: 10.5555/3314872.3314892
• 发表时间: 2019-02
• 期刊: 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
• 作者: Rodrigo C. O. Rocha;Pavlos Petoumenos;Zheng Wang;M. Cole;Hugh Leather

Autotuning OpenCL Workgroup Size for Stencil Patterns

自动调整模板图案的 OpenCL 工作组大小

• 发表时间: 2016
• 作者: Cummins C.

Iterative Compilation on Mobile Devices

移动设备上的迭代编译

• 发表时间: 2016
• 作者: Mpeis P

Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Hillsboro, OR, USA, September 15-17, 2014, Revised Selected Papers

并行计算的语言和编译器 - 第 27 届国际研讨会，LCPC 2014，美国俄勒冈州希尔斯伯勒，2014 年 9 月 15-17 日，修订后的精选论文

• DOI: 10.1007/978-3-319-17473-0_14
• 发表时间: 2015
• 作者: Emani M

Synthesizing benchmarks for predictive modeling

• DOI: 10.1109/cgo.2017.7863731
• 发表时间: 2017-02
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Chris Cummins;Pavlos Petoumenos;Zheng Wang-;Hugh Leather