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CS/ECE 3330 Computer Architecture

CS/ECE 3330 Computer Architecture. Chapter 1 Power / Parallelism. Last Time. Performance Analysis It’s all relative Make sure the units cancel out! What is a Hz? Amdahl’s Law Benchmarking. Battery life. Thermal issues: affect cooling, packaging, reliability, timing. Environment.

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CS/ECE 3330 Computer Architecture

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  1. CS/ECE 3330Computer Architecture Chapter 1 Power / Parallelism

  2. Last Time • Performance Analysis • It’s all relative • Make sure the units cancel out! • What is a Hz? • Amdahl’s Law • Benchmarking

  3. Battery life Thermal issues: affect cooling, packaging, reliability, timing Environment Why Worry about Power Dissipation?

  4. Power Trends • “The Power Wall”

  5. Power Dissipation Has Peaked • Must design with strict power envelopes • 130W servers, 65W desktop, 10-30W laptops, 1W mobile

  6. How Hot Does it Get?

  7. Cooling Issues http://www.youtube.com/watch?v=nYhEpHEPqcc

  8. Intel vs. Duracell • No Moore’s Law in batteries: 2-3%/year growth 16x Processor (MIPS) 14x 12x Improvement(compared to year 0) Hard Disk (capacity) 10x 8x Memory (capacity) 6x 4x Battery (energy stored) 2x 1x 0 1 2 3 4 5 6 Time (years)

  9. Environment • Environment Protection Agency (EPA): computers consume 10% of commercial electricity consumption • Includes peripherals, possibly also manufacturing • Data center growth was cited as a contribution to the 2000/2001 California Energy Crisis • Equivalent power (with only 30% efficiency) for AC • CFCs used for refrigeration • Lap burn • Fan noise

  10. Power Matters at Scale… [J. Koomey (LBL), 2007] Eric Schmidt, CEO of Google: "What matters most to the computer designers at Google is not speed, but power - low power, because data centers can consume as much electricity as a city."

  11. But Remember Amdahl’s Law

  12. Power vs. Energy

  13. Power vs. Energy • Power consumption in watts • Determines battery life in hours • Sets packaging limits • Energy efficiency in joules • Rate at which power is consumed over time • Energy = power * delay (joules = watts * seconds) • Lower energy number means less power to perform a computation at same frequency

  14. Another Fallacy: Low Power at Idle • X4 power benchmark • At 100% load: 295W • At 50% load: 246W (83%) • At 10% load: 180W (61%) • Google data center • Mostly operates at 10% – 50% load • At 100% load less than 1% of the time • Consider designing processors to make power proportional to load

  15. Capacitance: Function of wire length, transistor size Supply Voltage: Has been dropping with successive fab generations Frequency switched: Clock frequency + likelihood of change Capacitive Power Dissipation Power ~ C V2 f

  16. Reducing Power • Suppose a new CPU has • 75% of capacitive load of old CPU • 25% voltage and 25% frequency reduction • The power wall • We can’t reduce voltage further • We can’t remove more heat • How else can we improve performance?

  17. Uniprocessor Performance Constrained by power, instruction-level parallelism, memory latency

  18. Multiprocessors • Multicore microprocessors • More than one processor per chip • Multiprocessors and clusters – another course • Requires explicitly parallel programming • Compare with instruction-level parallelism • Hardware executes multiple instructions at once • Hidden from the programmer • Hard to do • Programming for performance • Load balancing • Optimizing communication and synchronization

  19. Multicore Architecture Examples 2 × quad-coreIntel Xeon e5345(Clovertown) 2 × quad-coreAMD Opteron X4 2356(Barcelona)

  20. Multicore Architecture Examples 2 × oct-coreSun UltraSPARCT2 5140 (Niagara 2) 2 × oct-coreIBM Cell QS20

  21. Key Points • Power has become a limiting factor • Power vs energy • P = C * (V^2) * F • One solution: Multicore processors • Different scale than “old” parallel processors • More detail in Chapter 7

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