Energy-efficient data centers: Exploiting knowledge about application and resources

424 views

Published on

Presentation by Jose M. Moya at the IEEE Region 8 SB & GOLD Congress (25 – 29 July, 2012).

The current techniques for data center energy optimization, based on
efficiency metrics like PUE, pPUE, ERE, DCcE, etc., do not take into
account the static and dynamic characteristics of the applications and
resources (computing and cooling). However, the knowledge about the
current state of the data center, the past history, the resource
characteristics, and the characteristics of the jobs to be executed
can be used very effectively to guide decision-making at all levels in
the datacenter in order to minimize energy needs. For example, the
allocation of jobs on the available machines, if done taking into
account the most appropriate architecture for each job from the
energetic point of view, and taking into account the type of jobs that
will come later, can reduce energy needs by 30%.

Moreover, to achieve significant reductions in energy consumption of
state-of-the-art data centers (low PUE) is becoming increasingly
important a comprehensive and multi-level approach, ie, acting on
different abstraction levels (scheduling and resource allocation,
application, operating system, compilers and virtual machines,
architecture, and technology), and at different scopes (chip, server,
rack, room, and multi-room).

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
424
On SlideShare
0
From Embeds
0
Number of Embeds
12
Actions
Shares
0
Downloads
23
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Energy-efficient data centers: Exploiting knowledge about application and resources

  1. 1. CAMPUS OF INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” Energy-efficient data centers: Exploiting knowledge about application and resources José M. Moya <jm.moya@upm.es> Integrated Systems Laboratory José M.Moya | Madrid (Spain), July 27, 2012 1
  2. 2. CAMPUS OF INTERNATIONAL EXCELLENCE Data centers“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 2
  3. 3. CAMPUS OF INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 3
  4. 4. CAMPUS OF INTERNATIONAL EXCELLENCE Power distribution“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 4
  5. 5. CAMPUS OF INTERNATIONAL EXCELLENCE Power distribution (Tier 4)“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 5
  6. 6. CAMPUS OF INTERNATIONAL EXCELLENCE Contents“Ingeniamos el futuro” • Motivation • Our approach – Scheduling and resource management – Virtual machine optimizations – Centralized management of low-power modes – Processor design • Conclusions José M.Moya | Madrid (Spain), July 27, 2012 6
  7. 7. CAMPUS OF INTERNATIONAL EXCELLENCE Motivation“Ingeniamos el futuro” • Energy consumption of data centers – 1.3% of worldwide energy production in 2010 – USA: 80 mill MWh/year in 2011 = 1,5 x NYC – 1 data center = 25 000 houses • More than 43 Million Tons of CO2 emissions per year (2% worldwide) • More water consumption than many industries (paper, automotive, petrol, wood, or plastic) Jonathan Koomey. 2011. Growth in Data center electricity use 2005 to 2010 José M.Moya | Madrid (Spain), July 27, 2012 7
  8. 8. CAMPUS OF INTERNATIONAL EXCELLENCE Motivation“Ingeniamos el futuro” 35000 World server installed base 30000• It is expected for total data 25000 (thousands) 20000 High-end servers center electricity use to 15000 Mid-range servers 10000 exceed 400 GWh/year by 5000 Volume servers 2015. 0 2000 2005 2010• The required energy for 5,75 Million new servers per year cooling will continue to be at 10% unused servers (CO2 emissions least as important as the similar to 6,5 million cars) energy required for the 300 computation. 250 Infrastructure (billion kWh/year) Electricity use 200 Communications• Energy optimization of future 150 Storage data centers will require a 100 High-end servers 50 Mid-range servers global and multi-disciplinary 0 Volume servers approach. 2000 2005 2010 José M.Moya | Madrid (Spain), July 27, 2012 8
  9. 9. CAMPUS OF Temperature-dependent INTERNATIONAL EXCELLENCE reliability problems“Ingeniamos el futuro” ✔ Electromigration (EM) ✖ Time-dependent dielectric- breakdown (TDDB) Stress migration (SM) ✖ ✖ Thermal cycling (TC) José M.Moya | Madrid (Spain), July 27, 2012 9
  10. 10. CAMPUS OF INTERNATIONAL EXCELLENCE Cooling a data center“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 10
  11. 11. CAMPUS OF INTERNATIONAL EXCELLENCE Server improvements“Ingeniamos el futuro” • Virtualization - 27% • Energy Star server conformance = 6.500 • Better capacity planning 2.500 José M.Moya | Madrid (Spain), July 27, 2012 11
  12. 12. CAMPUS OF INTERNATIONAL EXCELLENCE Cooling improvements“Ingeniamos el futuro” • Improvements in air flow management and wider temperature ranges Energy savings up to 25% 25.000 Return of investment in only 2 years José M.Moya | Madrid (Spain), July 27, 2012 12
  13. 13. CAMPUS OF INTERNATIONAL Infrastructure improvements EXCELLENCE“Ingeniamos el futuro” AC  DC – 20% reduction of power losses in the conversion process – 47 million dollars savings of real-state costs – Up to 97% efficiency, energy saving enough to power an iPad during 70 million years José M.Moya | Madrid (Spain), July 27, 2012 13
  14. 14. CAMPUS OF INTERNATIONAL EXCELLENCE Best practices“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 14
  15. 15. CAMPUS OF And… what about IT people? INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 15
  16. 16. CAMPUS OF PUE Power Usage Effectiveness INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” • State of the Art: PUE ≈ 1,2 – The important part is IT energy consumption – Current work in energy efficient data centers is focused in decreasing PUE – Decreasing PIT does not decrease PUE, but it is seen in the electricity bill • But how can we reduce PIT ? José M.Moya | Madrid (Spain), July 27, 2012 16
  17. 17. CAMPUS OF Potential energy savings by abstraction level INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 17
  18. 18. CAMPUS OF INTERNATIONAL EXCELLENCE Our approach“Ingeniamos el futuro” • Global strategy to allow the use of multiple information sources to coordinate decisions in order to reduce the total energy consumption • Use of knowledge about the energy demand characteristics of the applications, and characteristics of computing and cooling resources to implement proactive optimization techniques José M.Moya | Madrid (Spain), July 27, 2012 18
  19. 19. CAMPUS OF INTERNATIONAL EXCELLENCE Holistic approach“Ingeniamos el futuro” Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 19
  20. 20. CAMPUS OF 1. Room-level resource INTERNATIONAL EXCELLENCE management“Ingeniamos el futuro” Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 20
  21. 21. CAMPUS OF INTERNATIONAL Leveraging heterogeneity CCGrid 2012 EXCELLENCE“Ingeniamos el futuro” • Use heterogeneity to minimize energy consumption from a static/dynamic point of view – Static: Finding the best data center set-up, given a number of heterogeneous machines – Dynamic: Optimization of task allocation in the Resource Manager • We show that the best solution implies an heterogeneous data center – Most data centers are heterogeneous (several generations of computers) M. Zapater, J.M. Moya, J.L. Ayala. Leveraging Heterogeneity for Energy Minimization in Data Centers, CCGrid 2012 José M.Moya | Madrid (Spain), July 27, 2012 21
  22. 22. CAMPUS OF INTERNATIONAL EXCELLENCE Current scenario“Ingeniamos el futuro” Scheduler Resource WORKLOAD Manager Execution José M.Moya | Madrid (Spain), July 27, 2012 22
  23. 23. CAMPUS OF Potential improvements with best practices INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” Total power (computing and cooling) for various scheduling approaches 1400 max computing power, worst thermal placement min computing power, worst thermal placemenit optimal computing+cooling 1200 optimal computing+cooling, shut off idles optimal computing+cooling, shut off idles, no recirculation 1000 Power (KW) savings by minimizing computing power savings by minimizing the recirculation’s effect 800 savings by turning off idle machines unaddressed heat recirculation cost 600 basic (unavoidable) cost 400 200 0 0 20 40 60 80 100 job size relative to data center capacity (%) José operation cost (in kilowatts) for various “savings Fig. 3. Data center M.Moya | Madrid (Spain), July 27, 2012 23
  24. 24. energy consume energy consume 20 Cooling-aware scheduling and 100 15 CAMPUS OF resource allocation 10 INTERNATIONAL 50 EXCELLENCE 5 0 iMPACT Lab (Arizona State U) 0“Ingeniamos el futuro” FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT (a) (b) Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off 40 Energy consumption, Scenario (a) 40 jobs, 25014 core-hours, idle servers off Energy consumption, Scenario (a) 40 jobs, 25014 core-hours,energy cooling idle servers on cooling energy computing energy computing energy 40 cooling energy 300 Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 cooling energy jobs/hr 0.254 jobs/hr 35 computing energy 200 Turnaround time 8.98 hr computing energy 8.98 hr 12.17 hr 8.98 hr 48.49 hr Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 jobs/hr 0.427 jobs/hr Alg. runtime 170 ms 186 ms 397 ms 40.8 min 88.6 min 35 250 Energy savings 0.197 jobs/hr Throughput 0% 0.197 jobs/hr 1.7% 0.172 jobs/hr 4.1% 0.197 jobs/hr 3.6% 0.163 jobs/hr 4.7% 30 Turnaround time Throughput 8.98 hr 0.197 jobs/hr 8.98 hr 0.197 jobs/hr 12.17 hr 0.172 jobs/hr 8.98 hr 0.197 jobs/hr 17.75 hr 0.163 jobs/hr Alg. runtime 171 ms 186 ms 397 ms 42 min 100 min energy consumed (GJ) (GJ) energy consumed (GJ) (GJ) Turnaround time 18.41 hr 18.41 hr 20.75 hr 18.41 hr 51.75 hr Turnaround time 18.41 hr 18.41 hr 20.75 hr 18.41 hr 38.02 hr Alg. runtime 3.4 ms 6.9 ms 213 ms 23 min 40 min 30 Energy savings 0% 4.0% 14.6% 14.2% 15.1% 25 Alg. runtime 3.4 ms 6.9 ms 213 ms 23 min 43 min energy consumed energy consumed 150 200 Energy savings 0% 6.2% 8.6% 8.7% 10.2% Energy savings 0% 11.8% 54.7% 21.8% 60.5% 25 20 150 20 100 15 100 15 10 50 50 10 5 5 0 0 FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT 0 0 FCFS-FF (c) FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF (d) FCFS-LRH EDF-LRH FCFS-Xint SCINT Energy consumption, Scenario (c)(a) jobs, 45817 core-hours, idle servers on 174 Energy consumption, Scenario (c)(b) jobs, 45817 core-hours, idle servers off 174 Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on cooling energy Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off cooling energy computing energy computing energy 450 cooling energy 40 cooling energy Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr energy computing 0.561 jobs/hr 100 Throughput computing 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr energy 0.590 jobs/hr 400 Turnaround time 9.99 hr 9.99 hr 13.39 hr 9.99 hr 61.49 hr 300 Turnaround time 9.99 hrjobs/hr 9.99 hrjobs/hr 13.39 hr Throughput 0.580 0.580 9.99 hr 65.38 hr 0.349 jobs/hr 0.580 jobs/hr 0.254 jobs/hr 35 Alg. runtime 173 ms 191 ms 346 ms 21 min 147 min Alg. runtime time 173 ms Turnaround 8.98 hr 196 ms 8.98 hr 346 ms 12.17 hr 20 min 8.98 hr 142 min 48.49 hr Energy savings 0.0% 7.5% 17.3% 25.7% 41.4% 350 Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 jobs/hr 0.427 jobs/hr energy consumed (GJ) (GJ) energy consumed (GJ) (GJ) Energy savings 170 ms Alg. runtime 0% 2.5% 186 ms 5.9% 397 ms 9.4% 40.8 min 12.5% 88.6 min 80 250 Energy savings 0% 1.7% 4.1% 3.6% 4.7% 30 Turnaround time 8.98 hr 8.98 hr 12.17 hr 8.98 hr 17.75 hr 300 Alg. runtime 171 ms 186 ms 397 ms 42 min 100 min energy consumed energy consumed Energy savings 0% 4.0% 14.6% 14.2% 15.1% 25 250 200 60 200 20 150 40 150 15 100 100 20 10 50 50 5 0 0 FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT 0 0 FCFS-FF (e) FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF (f) FCFS-LRH EDF-LRH FCFS-Xint SCINT (c) (d) Fig. 8. Energy comparison of the simulated schemes for the three scenarios. The plots correspond in respective positions to the plots of Figure 7. Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers on Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers off José M.Moya | cooling energy (Spain), July 27, 2012 Madrid 24 cooling energy policy used in the data center, which enables energy execution as soon as they arrive if the queue is empty and the data 450 computing job computing energy Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr 0.561 jobs/hr 100 Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr 0.590 jobs/hr 400
  25. 25. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation LSI-UPM“Ingeniamos el futuro” Resource WORKLOAD Manager (SLURM) Execution Profiling and Energy Classification Optimization José M.Moya | Madrid (Spain), July 27, 2012 25
  26. 26. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation Scenario“Ingeniamos el futuro” • Workload: – 12 tasks from SPEC CPU INT 2006 – Random workload composed by 2000 tasks, divided into job sets – Random job set arrival time • Servers: José M.Moya | Madrid (Spain), July 27, 2012 26
  27. 27. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation Energy profiling“Ingeniamos el futuro” Resource WORKLOAD Manager (SLURM) Execution Profiling and Energy Classification Optimization Energy profiling José M.Moya | Madrid (Spain), July 27, 2012 27
  28. 28. CAMPUS OF INTERNATIONAL Workload characterization EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 28
  29. 29. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation“Ingeniamos el futuro” Optimization Resource WORKLOAD Manager (SLURM) Execution Profiling and Energy Classification Optimization Energy Minimization: • Minimization subjected to constraints • MILP problem (solved with CPLEX) • Static and Dynamic José M.Moya | Madrid (Spain), July 27, 2012 29
  30. 30. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation“Ingeniamos el futuro” Static optimization • Definition of optimal data center – Given a pool of 100 servers of each kind – 1 job set from workload – The optimizer chooses the best selection of servers – Constraints of cost and space Best solution: • 40 Sparc • 27 AMD Savings: • 5 a 22% energy • 30% time José M.Moya | Madrid (Spain), July 27, 2012 30
  31. 31. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation“Ingeniamos el futuro” Dynamic optimization • Optimal workload allocation – Complete workload (2000 tasks) – Good enough resource allocation in terms of energy (not the best) – Run-time evaluation and optimization Energy savings ranging from 24% to 47% José M.Moya | Madrid (Spain), July 27, 2012 31
  32. 32. CAMPUS OF Application-aware scheduling and INTERNATIONAL EXCELLENCE resource allocation“Ingeniamos el futuro” Conclusions • First proof-of-concept regarding the use of heterogeneity to save energy • Automatic solution • Automatic processor selection offers notable energy savings • Easy implementation in real scenarios – SLURM Resource Manager – Realistic workloads and servers José M.Moya | Madrid (Spain), July 27, 2012 32
  33. 33. CAMPUS OF 2. Server-level resource INTERNATIONAL EXCELLENCE management“Ingeniamos el futuro” Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 33
  34. 34. CAMPUS OF Scheduling and resource allocation INTERNATIONAL EXCELLENCE policies in MPSoCs “Ingeniamos el futuro” UCSD – System Energy Efficiency Lab A. Coskun , T. Rosing , K. Whisnant and K. Gross "Static and dynamic temperature- aware scheduling for multiprocessor SoCs", IEEE Trans. Very Large Scale Integr. Syst., vol. 16, no. 9, pp.1127 -1140 2008Fig. 3. Distribution of thermal hot spots, with with DPM (ILP). Fig. 3. Distribution of thermal hot spots, DPM (ILP). Fig. 4. Distribution of spatial gradients, with with DPM (ILP). Fig. 4. Distribution of spatial gradients, DPM (ILP). A. Static Scheduling TechniquesA. Static Scheduling Techniques hot spots. While Min-Th reduces the spatial differentials hot spots. While Min-Th reduces the highhigh spatial differentials We We next provideextensive comparison of the ILP ILP based above 15 we observe a substantial increase in the spatial next provide an an extensive comparison of the based above 15 C, C, we observe a substantial increase in the spatial José M.Moya | Min-Th&Sp. gradientstechniques. We refer to to static approach as as Madrid (Spain), July 27, 2012 above C. C. In contrast, method achieves lower techniques. We referour our static approach Min-Th&Sp. gradients above 10 10 In contrast,34 our method achieves lower our As discussedSection III, we implemented the ILP ILP min- and and more balanced temperature distribution in die. die.As discussed in in Section III, we implemented the for for min- more balanced temperature distribution in the the
  35. 35. CAMPUS OF Scheduling and resource allocation INTERNATIONAL EXCELLENCE policies in MPSoCs“Ingeniamos el futuro” • Energy characterization of applications allows to define proactive scheduling and resource allocation policies, minimizing hotspots • Hotspot reduction allows to raise cooling temperature +1oC means around 7% cooling energy savings José M.Moya | Madrid (Spain), July 27, 2012 35
  36. 36. CAMPUS OF 3. Application-aware and INTERNATIONAL EXCELLENCE resource-aware virtual machine“Ingeniamos el futuro” Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 36
  37. 37. CAMPUS OF JIT compilation in virtual machines INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” • Virtual machines compile (JIT compilation) the applications into native code for performance reasons • The optimizer is general- purpose and focused in performance optimization José M.Moya | Madrid (Spain), July 27, 2012 37
  38. 38. CAMPUS OF JIT compilation for energy minimization INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” Back-end Front-end Code generator Optimizer • Application-aware compiler – Energy characterization of applications and transformations – Application-dependent optimizer – Global view of the data center workload • Energy optimizer – Currently, compilers for high-end processors oriented to performance optimization José M.Moya | Madrid (Spain), July 27, 2012 38
  39. 39. CAMPUS OF Energy saving potential for the compiler (MPSoCs) INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” T. Simunic, G. de Micheli, L. Benini, and M. Hans. “Source code optimization and profiling of energy consumption in embedded systems,” International Symposium on System Synthesis, pages 193 – 199, Sept. 2000 – 77% energy reduction in MP3 decoder FEI, Y., RAVI, S., RAGHUNATHAN, A., AND JHA, N. K. 2004. Energy-optimizing source code transformations for OS-driven embedded software. In Proceedings of the International Conference VLSI Design. 261–266. – Up to 37,9% (mean 23,8%) energy savings in multiprocess applications running on Linux José M.Moya | Madrid (Spain), July 27, 2012 39
  40. 40. CAMPUS OF 4. Global automatic INTERNATIONAL EXCELLENCE management of low-power“Ingeniamos el futuro” modes Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 40
  41. 41. CAMPUS OF DVFS – Dynamic Voltage and Frequency Scaling INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” • As supply voltage decreases, power decreases quadratically • But delay increases (performance decreases) only linearly • The maximum frequency also decreases linearly • Currently, low-power modes, if used, are activated by inactivity of the server operating system José M.Moya | Madrid (Spain), July 27, 2012 41
  42. 42. CAMPUS OF INTERNATIONAL EXCELLENCE Room-level DVFS“Ingeniamos el futuro” • To minimize energy consumption, changes between modes should be minimized • There exist optimal algorithms for a known task set (YDS) • Workload knowledge allows to globally schedule low-power modes without any impact in performance José M.Moya | Madrid (Spain), July 27, 2012 42
  43. 43. CAMPUS OF INTERNATIONAL Parallelism to save energy EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 43
  44. 44. CAMPUS OF 5. Temperature-aware floorplanning of INTERNATIONAL EXCELLENCE MPSoCs and many-cores“Ingeniamos el futuro” Chip Server Rack Room Multi- room Sched & alloc 2 1 app OS/middleware Compiler/VM 3 architecture 4 4 technology 5 José M.Moya | Madrid (Spain), July 27, 2012 44
  45. 45. CAMPUS OF Temperature-aware floorplanning INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” José M.Moya | Madrid (Spain), July 27, 2012 45
  46. 46. Average MaxTemp reduction: 12 oC Potential energy savings CAMPUS OF Larger temperature reductions for benchmarks with floorplanning INTERNATIONAL with higher maximum temperature EXCELLENCE“Ingeniamos el futuro” For many benchmarks, temperature reducions are Y. Han, I. Koren, and C. A. Moritz. Temperature Aware Floorplanning. In Proc. of the larger than 20 oC Second Workshop on Temperature-Aware Computer Systems, June 2005 Maximum Temperature original modified 140 120 100 80 60 40 20 0 wupwise twolf swim gzip mgrid mcf lucas applu ammp bzip2 crafty fma3d perlbmk vortex avg apsi vpr equake facerec gcc mesa eon gap art parser – Up to 21oC reduction of maximum temperature – Mean: -12oC in maximum temperature – Better results in the most critical examples José M.Moya | Madrid (Spain), July 27, 2012 46
  47. 47. CAMPUS OF Temperature-aware INTERNATIONAL EXCELLENCE floorplanning in 3D chips“Ingeniamos el futuro” • 3D chips are getting interest due to: – Scalability: reduces 2D equivalent area – Performance: shorter wire length – Reliability: less wiring • Drawback: – Huge increment of hotspots compared with 2D equivalent designs José M.Moya | Madrid (Spain), July 27, 2012 47
  48. 48. CAMPUS OF Temperature-aware floorplanning in 3D chips INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” • Up to 30oC reduction per layer in a 3D chip with 4 layers and 48 cores José M.Moya | Madrid (Spain), July 27, 2012 48
  49. 49. CAMPUS OF There is still much more to be done INTERNATIONAL EXCELLENCE“Ingeniamos el futuro” • Smart Grids – Consume energy when everybody else does not – Decrease energy consumption when everybody else is consuming • Reducing the electricity bill – Variable electricity rates – Reactive power coefficient – Peak energy demand José M.Moya | Madrid (Spain), July 27, 2012 49
  50. 50. CAMPUS OF INTERNATIONAL EXCELLENCE Conclusions“Ingeniamos el futuro” • Reducing PUE is not the same as reducing energy consumption – IT energy consumption dominates in state-of-the-art data centers • Application and resources knowledge can be effectively used to define proactive policies to reduce the total energy consumption – At different levels – In different scopes – Taking into account cooling and computation at the same time • Proper management of the knowledge of the data center thermal behavior can reduce reliability issues • Reducing energy consumption is not the same as reducing the electricity bill José M.Moya | Madrid (Spain), July 27, 2012 50
  51. 51. CAMPUS OF INTERNATIONAL EXCELLENCE Contact“Ingeniamos el futuro” José M. Moya +34 607 082 892 jm.moya@upm.es ETSI de Telecomunicación, B104 Avenida Complutense, 30 Madrid 28040, Spain Gracias: José M.Moya | Madrid (Spain), July 27, 2012 51

×