ENPOWER

Lead Research Organisation: Queen's University of Belfast
Department Name: Electronics Electrical Eng and Comp Sci

Abstract

Energy efficiency is one of the primary design constraints for modern processing systems. Limited battery life and excessive internal power densities limit the number of transistors that can be active simultaneous in a silicon chip. Energy and power reduction in conventional computing is limited by the inability of modifying the architecture or adapting to changes in the fabrication process, temperature or application requirements after chip fabrication. When these changes are possible are limited by the need of "margining" that introduces safety margins so devices operate under worst conditions. Worst conditions are rarely the case an important energy and performance gains are possible if technology can adapt to the real conditions of operation. This research addresses this challenge by investigating energy proportional computing with a novel voltage, frequency and logic scaling triplet to adapt to changes in applications, fabrication or operating conditions. The results from this research are expected to deliver new fundamental insights to the question of: How future computers can obtain orders of magnitude higher performance with limited energy budgets?
 
Description The ENPOWER project is the first to explore power-proportional heterogeneous many-core platforms with reconfigurable fabric. The term power-proportional refers to the ability of the platform to execute a given workload with power consumption that is linearly proportional with the utilisation of the platform's hardware components. ENPOWER is also the first research activity to implement reconfigurable fabrics with power proportionality via voltage and frequency scaling, and the first to implement performance optimisation under power caps on these fabrics. ENPOWER contributes novel power-capped performance optimisation methods via fine-grain power modelling in conjunction with intelligent workload allocation between software and hardware. ENPOWER is finally the first project to construct and validate detailed models of the energy consumption of data transfers through memories and system-level interconnects.

Energy proportionality is crucial for modern computing systems. Despite significant advances in building more power-efficient computing systems by leveraging heterogeneity, effective and proportional power scaling of hardware resources is not well established yet. This is particularly true in embedded Systems-On-Chip with both general purpose processors (GPP) and field programmable gate array (FPGA) devices, an emerging class of processors with significant potential across embedded and high-performance computing.

To enable energy proportionality on such heterogeneous systems, it is essential to understand how different computation patterns affect energy consumption. In ENPOWER, we accomplish this task by associating compute kernels running in the system and the programming constructs used to express these kernels, with energy. We specifically use the OpenCL programming model and extend it to achieve energy aware kernel mapping to heterogeneous devices. We propose new power capping methods for adapting task kernel to the overall energy budget of heterogeneous systems. Our methods aim at achieving the highest performance under capped power consumption. We develop new power models for entire heterogeneous platforms with multi-thread and vector processing capabilities on GPPs and FPGAs. We demonstrate the effectiveness of these models in applications ranging from image processing to financial analytics and evaluate these models on commercial platforms, such as the ZYNQ7000 ZC702.

Our research outcome shows that we have achieved lower than 8% error in estimating the power consumption of highly complex heterogeneous platforms and sustained optimal power-capped performance on these platforms in 97.5% of all tested application scenarios. We have also improved power-capped performance by over 30% compared to state of the art approaches. By adopting a dominant heterogeneous programming model, namely OpenCL, we have also achieved productive and seamless power-aware programming model across GPPs and FPGAs.
Exploitation Route In 2017, the findings of the ENPOWER project were used for the implementation and optimisation of several algorithms on micro-servers, including credit risk analytics algorithms, and algorithms used in gaming environments where the gaming service is distributed between mobile devices and the Cloud, and graph algorithms. The ENPOWER findings were also used in a number of comparative studies of virtualisation technologies for accelerators based on GPUs and FPGAs. Last, the ENPOWER findings were used to understand the energy-efficiency implications of error mitigation and resilience management solutions on heterogeneous SoCs. In a broader context, the findings of the ENPOWER project provide pathways to cap, both statically and dynamically, the energy consumption of computing systems designed for both the embedded systems market and the server market. Importantly, ENPOWER will enable on-demand energy capping with performance guarantees set by the platform user or operator, in response to the application's computational and data access requirements. We are actively deploying the ENPOWER models and optimisers in four tiers of computing devices which underpin IoT applications, namely intelligent sensing devices, data acquisition devices, data pre-processing micro-servers at network edges, and high-end data analytics servers within datacentres.
Sectors Digital/Communication/Information Technologies (including Software),Energy

 
Description The findings of this award led to the proposal and funding of a Knowledge Transfer Partnership project with Crevinn Teoranta, an Irish company specialising in FPGA acceleration, silicon design and verification for various sectors including the healthcare, automotive and financial sectors. The project transferred knowledge of using OpenCL for productive deployment of algorithms on FPGAs.
First Year Of Impact 2017
Sector Digital/Communication/Information Technologies (including Software),Financial Services, and Management Consultancy
Impact Types Economic

 
Description EPSRC ICT Delivery Planning Workshops
Geographic Reach National 
Policy Influence Type Participation in a national consultation
 
Description Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres
Amount £140,710 (GBP)
Funding ID EP/M015742/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 01/2015 
End 12/2017
 
Description EU Horizon2020 Programme: AllScale: An Exascale Programming, Multi-objective Optimisation and Resilience Management Environment Based on Nested Recursive Parallelism.
Amount € 438,578 (EUR)
Funding ID 671603 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 10/2015 
End 09/2018
 
Description EU Horizon2020 Programme: ECOSCALE: Energy-Efficient Heterogeneous Computing at Scale
Amount € 696,750 (EUR)
Funding ID 671632 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 10/2015 
End 09/2018
 
Description EU Horizon2020 Programme: RAPID Heterogeneous Secure Multi-level Remote Acceleration Service for Low-Power Integrated Systems and Devices
Amount € 326,925 (EUR)
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 01/2015 
End 12/2017
 
Description EU Horizon2020 Programme: UniServer Project
Amount € 663,625 (EUR)
Funding ID 687628 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 02/2016 
End 01/2019
 
Description EU Horizon2020 Programme: Vineyard Project
Amount € 4,815,810 (EUR)
Funding ID 688540 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 02/2016 
End 01/2019
 
Description Heterogeneous parallel and distributed computing with Java
Amount £221,592 (GBP)
Funding ID EP/M015750/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 01/2015 
End 12/2017
 
Description Horizon2020 Programme
Amount € 5,999,510 (EUR)
Funding ID H2020-732631 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 01/2017 
End 12/2020
 
Description Collaboration with ARM and Applied Micro on micro-servers operating with extended voltage/frequency margins 
Organisation ARM Holdings
Country United Kingdom 
Sector Private 
PI Contribution New resource management methods at the operating system & hypervisor levels for leveraging extended operating margins of 64-bit ARM processors to improve system-wide energy-efficiency in micro-servers.
Collaborator Contribution Donation of experimental boards from Applied Micro (XGene) and ARM (Juno).
Impact Ongoing development of variation-aware hypervisors with enhanced resilience, power management and performance management capabilities.
Start Year 2016
 
Description Collaboration with ARM on fine-grain energy accounting tools 
Organisation ARM Holdings
Country United Kingdom 
Sector Private 
PI Contribution Partnership with ARM on developing energy-efficient system software and methods for accurate and fine-grain energy accounting in the Linux operating system. Our research team has contributed new probabilistic models for energy accounting at time scales which are finer than the power sensing or sampling periods of on-chip or off-chip power sensing instruments.
Collaborator Contribution ARM has scoped this collaboration and contributed in an advisory role via a series of online and face-to-face meetings with the ALEA, ENPOWER and GEMSCLAIM project consortia.
Impact Thread-level energy accounting tools for ARM Big.Little platforms (e.g. Exynos) and their use in energy-aware scheduling and resource allocation on mobile devices are currently in implementation and preliminary evaluation stages.
Start Year 2016
 
Description Collaboration with IBM on disaggregated memory technologies and near-data computing 
Organisation IBM
Country United States 
Sector Private 
PI Contribution Our research team has contributed methods to manage data caching and placement on disaggregated memory architectures with near-data processing elements.
Collaborator Contribution IBM has contributed novel remote memory server infrastructures and near-data acceleration technologies.
Impact Materialised through an industrial placement of QUB research staff, this partnership is exploring designs to substantially improve the energy-efficiency of large memory systems, via the use of disaggregation of memory, RDMA-based networking to remote memory devices and near-data accelerators for in-situ, in-memory analytics.
Start Year 2015
 
Description Credit Suisse 
Organisation Credit Suisse Group
Country Switzerland 
Sector Private 
PI Contribution Partnership with Credit Suisse explores design methods and tools to reduce the carbon and space footprint of datacentres that serve real-time financial analytics applications in London.
Collaborator Contribution The partner contributed time equivalent to 0.1FTE over a year to engage in meetings and a joint experimental campaign to evaluate tools developed in the context of the ALEA (EP/L000055/1,) ENPOWER (EP/L004232/1) and GEMSCLAIM (EP/K017594/1) projects.
Impact The collaboration has resulted in dissemination of datacentre energy measurement, energy accounting, and energy optimisation methods explored within the ALEA (EP/L000055/1), ENPOWER (EP/L004232/1) and GEMSCLAIM (EP/L017594/1) projects among stakeholders in the capital markets, as well as a preliminary evaluation of energy-efficient micro-servers based on heterogeneous many-core architectures and the ARM ecosystem, as an alternative to heavily overprovisioned servers in financial datacentres.
Start Year 2014
 
Title ALEA Energy Accounting Tool 
Description The ALEA profiler is a cross-platform statistical profiling tool for Linux, which provides time and energy profiling at the basic block level on Intel and ARM architectures (32 and 64 bit). Energy profiling is available for platforms with energy or power meters. Currently, ALEA supports all Intel platforms with enabled RAPL interface and ARM-based Odoroid-XU/Odroid-XU3 platforms. The tool can be used for profiling both sequential and multi-threaded applications. Energy and execution time accounting to source code is also supported for applications compiled with debugging information (DWARF). 
Type Of Technology Software 
Year Produced 2017 
Open Source License? Yes  
Impact ALEA is actively used by a number of groups in the UK and the US (e.g. Edinburgh, Lancaster, Virginia Tech, Old Dominion University) to perform targeted, energy-aware code optimisation on high-end computing systems. 
URL https://hpdc-gitlab.eeecs.qub.ac.uk/lmukhanov/alea-release.git
 
Description NVTV Interview on Superocmputing 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Media (as a channel to the public)
Results and Impact Interview in NVTV's Behind the Science program on Supercomputing as a technology with impact on our everyday lives.
Year(s) Of Engagement Activity 2015
URL http://www.nvtv.co.uk/shows/behind-the-science-dimitrios-nikolopoulos/