On understanding the energy consumption of ARM-based multicore servers

Performance Evaluation Review

Volumn 41, Issue 1 SPEC. ISS., 2013, Pages 267-278

  Tudor, Bogdan Marius ^a   Teo, Yong Meng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Analytical model; Energy; Low power; Multicore; Performance; Servers

Indexed keywords

COMPUTING APPLICATIONS; ENERGY; HIGH PERFORMANCE COMPUTING; LOW POWER; MULTI CORE; PARETO DISTRIBUTIONS; PERFORMANCE; WORKLOAD CHARACTERIZATION;

ANALYTICAL MODELS; CLOCKS; DISTRIBUTED COMPUTER SYSTEMS; ENERGY EFFICIENCY; MODELS; MULTICORE PROGRAMMING; SERVERS;

ENERGY UTILIZATION;

EID: 84880238891     PISSN: 01635999     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2494232.2465553     Document Type: Conference Paper

References (40)

1. Chip maker Calxeda receives $55 million to push ARM chips into the data center, Oct 2012. http://www.webcitation.org/6BSIjQzCM.
2. Dell Reaches for the Cloud With New Prototype ARM Server, PCWorld Magazine, May 2012. http://www.webcitation.org/6BVBj0Oyz.
3. Google Data Center Efficiency: How We Do It, Oct 2012. http://www.webcitation.org/6C8PjIMYd.
4. Uptime Institute 2012 Survey, Oct 2012. http://uptimeinstitute.com/2012- survey-results/.
5. D. G. Andersen et al. Fawn: a fast array of wimpy nodes. Proc of SOSP, pages 1-14, 2009.
6. B. Atikoglu et al. Workload analysis of a large-scale key-value store. Proc of SIGMETRICS/PERFORMANCE, pages 53-64, 2012.
7. L. A. Barroso and U. Hölzle. The case for energy-proportional computing. Computer, 40(12):33-37, Dec. 2007.
8. A. Carroll and G. Heiser. An analysis of power consumption in a smartphone. Proc of USENIX ATC, pages 21-21, 2010.
9. J. Corbet et al. Linux Device Drivers, 3rd Edition. O'Reilly Media, Inc., 2005.
10. M. Curtis-Maury et al. Prediction models for multi-dimensional power-performance optimization on many cores. Proc of PACT, pages 250-259, 2008.
11. F. M. David et al. Context Switch Overheads for Linux on ARM Platforms. Proc. of ExpCS, 2007.
12. J. D. Davis et al. Maximizing cmp throughput with mediocre cores. Proc of PACT, pages 51-62, 2005
13. A. Gandhi et al. Optimal power allocation in server farms. Proc of SIGMETRICS, pages 157-168, 2009.
14. A. Gandhi et al. Are sleep states effective in data centers? Proc of IGCC, pages 1-10, 2012.
15. D. Gmach et al. Workload analysis and demand prediction of enterprise data center applications. Proc of IISWC, pages 171-180, 2007.
16. J. L. Hennessy and D. A. Patterson. Computer Architecture, Fourth Edition: A Quantitative Approach. 2006.
17. M. Hill and M. Marty. Amdahl's Law in the Multicore Era. Computer, 41(7):33-38, 2008.
18. U. Hölzle. Brawny cores still beat wimpy cores, most of the time. IEEE Micro, 30(4), 2010.
19. T. Horvath and K. Skadron. Multi-mode energy management for multi-tier server clusters. Proc of PACT, pages 270-279, 2008.
20. Y. Hu et al. I/o scheduling model of virtual machine based on multi-core dynamic partitioning. Proc of HPDC, pages 142-154, 2010.
21. V. Kumar and A. Fedorova. Towards better performance per watt in virtual environments on asymmetric single-isa multi-core systems. SIGOPS Oper. Syst. Rev., 43(3):105-109, July 2009.
22. E. Le Sueur and G. Heiser. Dynamic voltage and frequency scaling: The laws of diminishing returns. Proc of HotPower, 2010.
23. E. Le Sueur and G. Heiser. Slow down or sleep, that is the question. Proc of USENIX ATC, 2011.
24. A. W. Lewis et al. Runtime energy consumption estimation for server workloads based on chaotic time-series approximation. ACM Trans. Archit. Code Optim., 9(3):15:1-15:26, Oct. 2012.
25. K. Lim et al. Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments. Proc of ISCA, pages 315-326, 2008.
26. M. Y. Lim et al. Softpower: fine-grain power estimations using performance counters. Proc of HPDC, pages 308-311, 2010.
27. F. Liu et al. Understanding How Off-chip Memory Bandwidth Partitioning in Chip Multiprocessors Affects System Performance. Proc of HPCA, 2010.
28. C. W. Lively et al. Power-aware predictive models of hybrid (mpi/openmp) scientific applications on multicore systems. Computer Science - R&D, 27(4):245-253, 2012.
29. P. Lotfi-Kamran et al. Scale-out Processors. Proc of ISCA, pages 500-511, 2012.
30. K. Malladi et al. Towards energy-proportional datacenter memory with mobile dram. Proc of ISCA, pages 37-48, 2012.
31. D. Meisner et al. PowerNap: Eliminating Server Idle Power. Proc of ASPLOS, pages 205-216, 2009.
32. D. Meisner et al. Power management of online data-intensive services. Proc of ISCA, pages 319-330, 2011.
33. R. Mijat. System level benchmarking analysis of the cortextm-a9 mpcore. ARM Connected Community Technical Symposium, 2009.
34. D. Molka et al. Memory Performance and Cache Coherency Effects on an Intel Nehalem Multiprocessor System. Proc of PACT, pages 261-270, 2009.
35. A. S. Szalay et al. Low-power Amdahl-balanced blades for data intensive computing. SIGOPS Oper. Syst. Rev., 44(1):71-75, Mar. 2010.
36. Y. C. Tay. Analytical Performance Modeling for Computer Systems. Synthesis Lectures on Computer Science. Morgan & Claypool Publishers, 2010.
37. B. M. Tudor and Y. M. Teo. A practical approach for performance analysis of shared-memory programs. Proc of IPDPS, pages 652-663, 2011
38. A. Verma et al. Server workload analysis for power minimization using consolidation. Proc of USENIX ATC, 2009.
39. M. Weiser et al. Scheduling for reduced cpu energy. Proc of OSDI, 1994.
40. A. Weissel and F. Bellosa. Process cruise control: event-driven clock scaling for dynamic power management. Proc of CASES, pages 238-246, 2002.