Core Level Utilization for Achieving Energy Efficiency in Heterogeneous Systems

Energy budget is becoming a constraint in all computing systems. From mobile systems to supercomputers, the focus has shifted from performance to energy and power efficiency. Design metrics are not anymore solely based on performance, as the energy efficiency of application executions is becoming a predominant design requirement. In addition to established voltage and frequency scaling techniques, several semiconductor chip manufactures introduced heterogeneous multi-core processors to increase the level of energy efficiency. The usage of this heterogeneity is complicated by the scheduling and mapping decisions needed to be made at run-time for application execution. In order to exploit the full potential of such architectures we need to make the right decisions, because parameters such as type of core, frequency and utilization usually affect the power dissipation and performance. This paper analyses achievable energy gains when exploiting core level utilization in addition to other control techniques such as: heterogeneity, voltage and frequency scaling. We build an energy efficiency model based on platform configurations defined by core types, the different voltage and frequency levels and the core utilization rate. Based on the built model, we analyze the energy efficiency variations for different platform configurations providing the same level of performance. We show that trading the number and type of core with frequency and voltage level and core utilization rate can lead to substantial energy efficiency gains.

[1]  Michael Taylor A landscape of the new dark silicon design regime , 2013 .

[2]  Luca Abeni,et al.  Deadline scheduling in the Linux kernel , 2016, Softw. Pract. Exp..

[3]  Simon Holmbacka,et al.  Energy efficiency and performance management of parallel dataflow applications , 2014, Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing.

[4]  Marco D. Santambrogio,et al.  Workload-aware power optimization strategy for asymmetric multiprocessors , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[5]  K. Steinhubl Design of Ion-Implanted MOSFET'S with Very Small Physical Dimensions , 1974 .

[6]  Amit Kumar Singh,et al.  Mapping on multi/many-core systems: Survey of current and emerging trends , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[7]  Erwan Nogues,et al.  Energy Efficient Scheduling of Real Time Signal Processing Applications through Combined DVFS and DPM , 2016, 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP).

[8]  Anuj Pathania,et al.  Price theory based power management for heterogeneous multi-cores , 2014, ASPLOS.

[9]  Israel Koren,et al.  An opportunistic prediction-based thread scheduling to maximize throughput/watt in AMPs , 2013, Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques.

[10]  Lieven Eeckhout,et al.  Scheduling heterogeneous multi-cores through performance impact estimation (PIE) , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).

[11]  Hsien-Hsin S. Lee,et al.  Extending Amdahl's Law for Energy-Efficient Computing in the Many-Core Era , 2008, Computer.

[12]  James C. Hoe,et al.  Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs? , 2010, 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.

[13]  Simon Holmbacka,et al.  Thermal influence on the energy efficiency of workload consolidation in many-core architectures , 2013, 2013 24th Tyrrhenian International Workshop on Digital Communications - Green ICT (TIWDC).

[14]  Nikil D. Dutt,et al.  SmartBalance: A sensing-driven linux load balancer for energy efficiency of heterogeneous MPSoCs , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[15]  Henry Hoffmann,et al.  Minimizing energy under performance constraints on embedded platforms: resource allocation heuristics for homogeneous and single-ISA heterogeneous multi-cores , 2015, SIGBED.

[16]  Scott A. Mahlke,et al.  Heterogeneous microarchitectures trump voltage scaling for low-power cores , 2014, 2014 23rd International Conference on Parallel Architecture and Compilation (PACT).

[17]  Antonia Zhai,et al.  Energy efficient speculative threads: Dynamic thread allocation in same-ISA heterogeneous multicore systems , 2010, 2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT).

[18]  Jeffrey S. Vetter,et al.  A Survey of CPU-GPU Heterogeneous Computing Techniques , 2015, ACM Comput. Surv..

[19]  Gang Chen,et al.  Energy-efficient mapping of real-time streaming applications on cluster heterogeneous MPSoCs , 2015, 2015 13th IEEE Symposium on Embedded Systems For Real-time Multimedia (ESTIMedia).

[20]  Christian Bienia,et al.  PARSEC 2.0: A New Benchmark Suite for Chip-Multiprocessors , 2009 .