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CSE 691: Energy-Efficient Computing Lecture 5 SPEED: processor. Anshul Gandhi 1307, CS building anshul@cs.stonybrook.edu. opt_allocation paper. U.S. Data Center Energy Consumption. 120 billion kWh. 50 billion kWh. kWh (in billions) . $ 8.4 billion. 12 billion kWh.
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CSE 691: Energy-Efficient ComputingLecture 5SPEED: processor Anshul Gandhi 1307, CS building anshul@cs.stonybrook.edu
U.S. Data Center Energy Consumption 120 billion kWh 50 billion kWh kWh (in billions) $ 8.4 billion 12 billion kWh Source: EPA report to Congress on Server and Data Center Energy Efficiency ,2007
Goal Get the best performance from the power, P, that we have. Data Center P
GoalHow to split P to minimize mean response time? Right answer can improve performance by up to 5X P1 P P2 P3 Constraint: P ≥ P1 + P2 + P3
Our Experimental Results How power affects server speed for a single server DFS: Dynamic Frequency Scaling Frequency (GHz) (server speed) DFS “linear” P = system power NOT processor power Power (Watts)
Our Experimental Results How power affects server speed for a single server DVFS DVFS +DFS “LINPACK” CPU BOUND Frequency (GHz) DFS Frequency (GHz) Frequency (GHz) Power (Watts) Power (Watts) Power (Watts) DVFS DVFS +DFS DFS “STREAM” MEM BOUND Frequency (GHz) Frequency (GHz) Frequency (GHz) Power (Watts) Power (Watts) Power (Watts)
Power Allocation Results DFS Frequency (GHz) Power (Watts)
Power Allocation Results DVFS Frequency (GHz) Power (Watts)
Power Allocation Results DVFS +DFS Frequency (GHz) Power (Watts)
Power Allocation Results DFS Mean Resp. Time (sec) Arrival rate (jobs/sec) DVFS DVFS+DFS Mean Resp. Time (sec) Mean Resp. Time (sec) Arrival rate (jobs/sec) Arrival rate (jobs/sec)
Conclusions: How to allocate power optimally Speed Scaling? Linear, Steep Linear, Flat Cubic Arrival Rate? Arrival Rate? Arrival Rate? High Low Low High High Low PowMax PowMax PowMax PowMin PowMax PowMed