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

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

Power Trends “The Power Wall”

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

How Hot Does it Get?

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

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

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 Chlorofluorocarbons (CFCs) cited as the cause of ozone depletion

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."

But Remember Amdahl’s Law

Power vs. Energy

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

Another Fallacy: Low Power at Idle Morgan Kaufmann Publishers April 23, 2017 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 Chapter 1 — Computer Abstractions and Technology

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

Morgan Kaufmann Publishers April 23, 2017 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? Chapter 1 — Computer Abstractions and Technology

Uniprocessor Performance Morgan Kaufmann Publishers April 23, 2017 Uniprocessor Performance Constrained by power, instruction-level parallelism, memory latency Chapter 1 — Computer Abstractions and Technology

Morgan Kaufmann Publishers April 23, 2017 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 Traditional multiprocessors and clusters are covered in detail in another course Chapter 1 — Computer Abstractions and Technology

Multicore Architecture Examples Morgan Kaufmann Publishers 23 April, 2017 Multicore Architecture Examples 2 × quad-core Intel Xeon e5345 (Clovertown) 2 × quad-core AMD Opteron X4 2356 (Barcelona) Chapter 7 — Multicores, Multiprocessors, and Clusters

Multicore Architecture Examples Morgan Kaufmann Publishers 23 April, 2017 Multicore Architecture Examples 2 × oct-core Sun UltraSPARC T2 5140 (Niagara 2) 2 × oct-core IBM Cell QS20 Chapter 7 — Multicores, Multiprocessors, and Clusters

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