Connor Imes
Abstract
This paper explores the problem of energy optimization in embedded platforms. Specifically, it studies resource allocation strategies for meeting performance constraints with minimal energy consumption. We present a comparison of solutions for both homogeneous and single-ISA heterogeneous multi-core embedded systems. We demonstrate that different hardware platforms have fundamentally different performance/energy tradeoff spaces. As a result, minimizing energy on these platforms requires substantially different resource allocation strategies. Our investigations reveal that one class of systems requires a race-to-idle heuristic to achieve optimal energy consumption, while another requires a never-idle heuristic to achieve the same. The differences are dramatic: choosing the wrong strategy can increase energy consumption by over 2× compared to optimal.
- S. Albers. "Algorithms for Dynamic Speed Scaling". In: STACS. 2011, pp. 1--11.Google Scholar
- S. Albers and A. Antoniadis. "Race to idle: new algorithms for speed scaling with a sleep state". In: SODA. 2012. Google ScholarDigital Library
- L. A. Barroso and U. Hölzle. "The Case for Energy-Proportional Computing". In: IEEE Computer 40 (2007). Google ScholarDigital Library
- C. Bienia et al. "The PARSEC Benchmark Suite: Characterization and Architectural Implications". In: PACT. 2008. Google ScholarDigital Library
- A. Carroll and G. Heiser. "Mobile Multicores: Use Them or Waste Them". In: Proceedings of the 2013 Workshop on Power Aware Computing and Systems (HotPower'13). Farmington, PA, USA, 2013, p. 12. Google ScholarDigital Library
- HardKernel. http://www.hardkernel.com/main/products/prdt\_info.php?g\_code=G137463363079.Google Scholar
- U. Hoelzle and L. A. Barroso. The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. 1st. Morgan and Claypool Publishers, 2009. Google ScholarDigital Library
- H. Hoffmann. "Racing vs. Pacing to Idle: A Comparison of Heuristics for Energy-aware Resource Allocation". In: HotPower. 2013. Google ScholarDigital Library
- H. Hoffmann et al. "Self-aware computing in the Angstrom processor". In: DAC. 2012. Google ScholarDigital Library
- H. Hoffmann et al. "A Generalized Software Framework for Accurate and Efficient Managment of Performance Goals". In: EMSOFT. 2013. Google ScholarDigital Library
- T. Instruments. http://www.ti.com/product/ina231.Google Scholar
- S. Irani et al. "Algorithms for Power Savings". In: ACM Trans. Algorithms 3.4 (Nov. 2007). Google ScholarDigital Library
- B. Jeff. "Big.LITTLE system architecture from ARM: saving power through heterogeneous multiprocessing and task context migration". In: DAC. 2012, pp. 1143--1146.Google ScholarCross Ref
- R. Kumar et al. "Processor Power Reduction Via Single-ISA Heterogeneous Multi-Core Architectures". In: Computer Architecture Letters 2.1 (2003), pp. 2--2. issn: 1556-6056. doi: 10.1109/L-CA.2003.6. Google ScholarDigital Library
- E. Le Sueur and G. Heiser. "Slow Down or Sleep, That is the Question". In: Proceedings of the 2011 USENIX Annual Technical Conference. Portland, OR, USA, 2011. Google ScholarDigital Library
- J. D. Lin et al. "Real-energy: A New Framework and a Case Study to Evaluate Power-aware Real-time Scheduling Algorithms". In: ISLPED. 2010. Google ScholarDigital Library
- M. Maggio et al. "Power Optimization in Embedded Systems via Feedback Control of Resource Allocation". In: IEEE Trans. on Control Systems Technology 21.1 (2013).Google ScholarCross Ref
- D. Meisner et al. "Power management of online data-intensive services". In: ISCA (2011). Google ScholarDigital Library
- A. Miyoshi et al. "Critical Power Slope: Understanding the Runtime Effects of Frequency Scaling". In: ICS. 2002. Google ScholarDigital Library
- T. Pering et al. "The simulation and evaluation of dynamic voltage scaling algorithms". In: ISLPED. 1998. Google ScholarDigital Library
- S. Saewong and R. Rajkumar. "Practical voltage-scaling for fixed-priority RT-systems". In: RTAS. 2003. Google ScholarDigital Library
- M. Weiser et al. "Scheduling for reduced CPU energy". In: Mobile Computing (1996).Google Scholar
Index Terms
- Minimizing energy under performance constraints on embedded platforms: resource allocation heuristics for homogeneous and single-ISA heterogeneous multi-cores
Recommendations
Racing and pacing to idle: an evaluation of heuristics for energy-aware resource allocation
HotPower '13: Proceedings of the Workshop on Power-Aware Computing and SystemsWe examine the problem of assigning computing resources to an application to meet a performance goal while minimizing energy consumption. We present a general formulation of this problem as a linear program, discuss several potential heuristic solutions,...
Trade-off between Performance and Energy Management in Autonomic and Green Data Centers
NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & SecurityIn recent years, energy optimization of cloud data centers got important consideration since data centers in activity frequently expend huge energy rates. In fact, the expanding processing capacity of data centers and its complexity, increases ...
An Analysis Framework for Investigating the Trade-Offs between System Performance and Energy Consumption in a Heterogeneous Computing Environment
IPDPSW '13: Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD ForumRising costs of energy consumption and an ongoing effort for increases in computing performance are leading to a significant need for energy-efficient computing. Before systems such as supercomputers, servers, and datacenters can begin operating in an ...
Comments