SchedUling on heterogeneous Mobile Multicores based on quality of ExpeRience
基于体验质量的异构移动多核调度
基本信息
- 批准号:EP/P003915/1
- 负责人:
- 金额:$ 12.87万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2016
- 资助国家:英国
- 起止时间:2016 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Users want mobile devices that appear fast and responsive, but at the same time have long lasting batteries and do not overheat. Achieving both of these at once is difficult. The workloads employed to evaluate mobile optimisations are rarely representative of real mobile applications and are oblivious to user perception, focussing only on performance. As a result hardware and software designers' decisions do not respect the user's Quality of Experience (QoE). The device either runs faster than necessary for optimal QoE, wasting energy, or the device runs too slowly, spoiling QoE. SUMMER will develop the first framework to record, replay, and analyse mobile workloads that represent and measure real user experience. Our work will expose for the first time the real Pareto trade-off between the user's QoE and energy consumption. The results of this project will permit others, from computer architects up to library developers, to make their design decisions with QoE as their optimisation target. To show the power of this new approach, we will design the first energy efficient operating system scheduler for heterogeneous mobile processors which takes QoE into account. With heterogeneous mobile processors just now entering the market, a scheduler able to use them optimally is urgently needed. We expect our scheduler to be at least 50% more energy efficient on average than the standard Linux scheduler on an ARM BIG.LITTLE system.
用户希望移动的设备看起来速度快、反应灵敏,但同时要有持久的电池,不会过热。要同时做到这两点是很困难的。用于评估移动的优化的工作负载很少能代表真实的移动的应用程序,并且忽略了用户的感知,只关注性能。因此,硬件和软件设计人员的决策不尊重用户的体验质量(QoE)。设备要么运行得比最佳QoE所需的更快,浪费能量,要么设备运行得太慢,破坏QoE。SUMMER将开发第一个框架来记录、回放和分析代表和衡量真实的用户体验的移动的工作负载。我们的工作将首次揭示用户的QoE和能耗之间的真实的帕累托权衡。这个项目的结果将允许其他人,从计算机架构师到库开发人员,以QoE作为他们的优化目标来做出设计决策。为了展示这种新方法的强大功能,我们将为考虑QoE的异构移动的处理器设计第一个节能操作系统调度器。随着异构移动的处理器刚刚进入市场,调度器能够最佳地使用它们是迫切需要的。我们希望我们的调度器在ARM BIG.LITTLE系统上比标准Linux调度器平均节能至少50%。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Vectorization-aware loop unrolling with seed forwarding
- DOI:10.1145/3377555.3377890
- 发表时间:2020-02
- 期刊:
- 影响因子:0
- 作者:Rodrigo C. O. Rocha;Vasileios Porpodas;Pavlos Petoumenos;L. F. Góes;Zheng Wang;M. Cole;Hugh Leather
- 通讯作者:Rodrigo C. O. Rocha;Vasileios Porpodas;Pavlos Petoumenos;L. F. Góes;Zheng Wang;M. Cole;Hugh Leather
Effective function merging in the SSA form
SSA形式的有效功能合并
- DOI:10.1145/3385412.3386030
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Rocha R
- 通讯作者:Rocha R
HyFM: function merging for free
HyFM:免费功能合并
- DOI:10.1145/3461648.3463852
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Rocha R
- 通讯作者:Rocha R
Synthesizing benchmarks for predictive modeling
- DOI:10.1109/cgo.2017.7863731
- 发表时间:2017-02
- 期刊:
- 影响因子:10.9
- 作者:Chris Cummins;Pavlos Petoumenos;Zheng Wang-;Hugh Leather
- 通讯作者:Chris Cummins;Pavlos Petoumenos;Zheng Wang-;Hugh Leather
Function Merging by Sequence Alignment
- DOI:10.5555/3314872.3314892
- 发表时间:2019-02
- 期刊:
- 影响因子:0
- 作者:Rodrigo C. O. Rocha;Pavlos Petoumenos;Zheng Wang;M. Cole;Hugh Leather
- 通讯作者:Rodrigo C. O. Rocha;Pavlos Petoumenos;Zheng Wang;M. Cole;Hugh Leather
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Hugh Leather其他文献
A graph neural network-based performance model for deep learning applications
用于深度学习应用的基于图神经网络的性能模型
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Shikhar Singh;James Hegarty;Hugh Leather;Benoit Steiner - 通讯作者:
Benoit Steiner
The Lambda Calculus: Practice and Principle
Lambda 演算:实践和原理
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Hugh Leather;J. Irgens - 通讯作者:
J. Irgens
Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization
交互式应用程序上的对象交叉捕获可驱动基于众包重放的编译器优化
- DOI:
10.1145/3517338 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Paschalis Mpeis;Pavlos Petoumenos;K. Hazelwood;Hugh Leather - 通讯作者:
Hugh Leather
Auto-Tuning Parallel Skeletons
自动调整平行骨架
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0.4
- 作者:
Alexander Collins;Christian Fensch;Hugh Leather - 通讯作者:
Hugh Leather
ALEA: A Fine-Grain Energy Profiling Tool
ALEA:细粒度能量分析工具
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
L. Mukhanov;Pavlos Petoumenos;Zheng Wang;K. Parasyris;Dimitrios S. Nikolopoulos;B. D. Supinski;Hugh Leather - 通讯作者:
Hugh Leather
Hugh Leather的其他文献
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