CSE 591: Energy-Efficient Computing Lecture 3 SPEED: processor Anshul Gandhi 347, CS building

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Presentation transcript:

CSE 591: Energy-Efficient Computing Lecture 3 SPEED: processor Anshul Gandhi 347, CS building

opt_allocation paper

U.S. Data Center Energy Consumption 3 $ 8.4 billion kWh (in billions)  120 billion kWh 12 billion kWh 50 billion kWh Source: EPA report to Congress on Server and Data Center Energy Efficiency,2007

4 P Get the best performance from the power, P, that we have. Goal Data Center

5 P P1P1 P2P2 P3P3 Goal How to split P to minimize mean response time? Right answer can improve performance by up to 5X Constraint: P ≥ P 1 + P 2 + P 3

Our Experimental Results DFS: Dynamic Frequency Scaling Power (Watts) DFS 6 Frequency (GHz) (server speed) How power affects server speed for a single server “linear” P = system power NOT processor power

Our Experimental Results Power (Watts) DFS 7 Frequency (GHz) How power affects server speed for a single server Power (Watts) Frequency (GHz) Power (Watts) Frequency (GHz) DVFS +DFS Power (Watts) DFS Frequency (GHz) Power (Watts) Frequency (GHz) Power (Watts) Frequency (GHz) DVFS +DFS “LINPACK” CPU BOUND “STREAM” MEM BOUND

Power Allocation Results 8 CPU bound “LINPACK” Memory bound “STREAM” DFS DVFS DVFS+DFS Power (Watts) DFS Frequency (GHz)

Power Allocation Results 9 CPU bound “LINPACK” Memory bound “STREAM” DFS DVFS DVFS+DFS Power (Watts) Frequency (GHz) DVFS

Power Allocation Results 10 CPU bound “LINPACK” Memory bound “STREAM” DFS DVFS DVFS+DFS Power (Watts) Frequency (GHz) DVFS +DFS

Power Allocation Results 11 CPU bound “LINPACK” Memory bound “STREAM” DFS DVFS DVFS+DFS DFS DVFS Arrival rate (jobs/sec) Mean Resp. Time (sec) Arrival rate (jobs/sec)

Conclusions: How to allocate power optimally 12 Speed Scaling? Arrival Rate? Linear, SteepLinear, FlatCubic Arrival Rate? PowMax PowMin High LowHigh Low High Low PowMax PowMed