Texas Tech University

Texas Tech University
Faster Computers = More Energy
• Moore’s law predicted 2 fold yearly increase in transistor count for inexpensive devices
•
Transistor size has decreased to the point where size can longer be major factor in speed
•
Multicore processors now fairly common
•
Increased performance from larger transistor counts and multiple cores has increased energy usage

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Faster Computers = More Energy
•
An hour of usage on a super computer today uses the same amount of energy that a moderate home will during the most extreme months of the year
•
Google estimates their data centers use the same amount of power as 200,000 homes each year.

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Energy Aware Motivations
•
Energy Costs
•
Device Battery Life
•
Green Computing Initiatives

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Energy Aware Research
•
Most work being done in hardware design
•
CPUs now have multiple operating states to save energy when not in use
•
Advanced Control Power Interface(ACPI) was developed to give Operating Systems the ability to reduce power consumption of computers
•
Most models & scheduling techniques rely on altering
CPU operating frequency, which user applications cannot directly access

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CPU Energy Usage
•
Energy is the amount of power used for a specified amount of time, 𝐸 = 𝑃 ∗ 𝑡
•
If the power varies with time then,
𝐸 = 𝑃 𝑡 𝑑𝑡
•
With N processors, the total energy is the sum of each processor’s usage, 𝐸 =
𝑁 𝑖=0
𝑃 𝑖 𝑡 𝑑𝑡

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CPU Energy Usage (continued)
•
The electrical power of a CPU is estimated as 𝑃 = 𝐶𝑉 2 𝐹 ,
𝐶𝑉 2 is a physical constant and F is the operating frequency.
•
As the frequency of a processor can vary with time, the energy usage of a multicore processor is 𝐸 =
𝑁 𝑖=0
𝐶𝑉
2 𝐹 𝑡 𝑑𝑡
•
CPUs only operate at S number of frequencies, 𝐸 =
𝑁 𝑖=0
𝑆 𝑗=0
𝐶𝑉 2 𝑓 𝑗 𝑡 𝑖𝑗
•
Developers cannot select the frequency of the CPU, only if it is idle or not, so there are only 2 frequencies we consider, ON & OFF, 𝐸 =
𝑁 𝑖=0
𝐶𝑉 2 (𝑓
𝑂𝑁 𝑡 𝑖𝑂𝑁
+ 𝑓
𝑂𝐹𝐹 𝑡 𝑖𝑂𝐹𝐹
)

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Sequential Application Energy
•
Sequential Applications only use 1 processor, so the other (N-1) processors are idle.
•
The energy usage is reduced to 𝐸 =
𝐶𝑉 2 (𝑁𝑓
𝑂𝐹𝐹 𝑡
𝑂𝑁
+ 𝑡
𝑂𝐹𝐹
+ 𝑡
𝑂𝑁
(𝑓
𝑂𝑁
− 𝑓
𝑂𝐹𝐹
))

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Amdahl’s Law
•
Can be used to give a comparison between sequential & parallel application performance
•
For this model, it gives us 𝐸
𝑁
, the ratio of the sequential energy usage to the parallel energy usage on an N processor system.
• 𝐸
𝑁
=
𝐶𝑉
2
(𝑁𝑓
𝑂𝐹𝐹
𝑁 𝑖=0 𝑡
𝑂𝑁
+𝑡
𝑂𝐹𝐹
𝐶𝑉 2 (𝑓
𝑂𝑁 𝑡 𝑖𝑂𝑁
+𝑓
𝑂𝐹𝐹
• 𝐶𝑉 2 is constant, so 𝐸
(𝑁𝑓
𝑂𝐹𝐹 𝑡
𝑂𝑁
+𝑡
𝑂𝐹𝐹
+𝑡
𝑂𝑁
𝑁
(𝑓
=
𝑂𝑁
+𝑡
𝑂𝑁
(𝑓
𝑂𝑁
−𝑓
𝑂𝐹𝐹
))
−𝑓
𝑂𝐹𝐹
)) 𝑡 𝑖𝑂𝐹𝐹
)
𝑁 𝑖=0
(𝑓
𝑂𝑁 𝑡 𝑖𝑂𝑁
+𝑓
𝑂𝐹𝐹 𝑡 𝑖𝑂𝐹𝐹
)

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Observations
•
Increasing CPU utilization increases Energy
Efficiency
• “Racing to idle” means that the CPU will return to an idle state sooner
•
Less time executing also means other components will be using less energy too

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Turning Off Idle Processors
•
If 𝑓
𝑂𝐹𝐹 is zero, then a parallel application uses the same power as its sequential version
•
If runtime is fixed, additional processors are unnecessary
•
Idle CPUs are not turned off and only waste energy
•
Newer devices have too many CPUs, i.e.
Smart Cell Phones

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No Idle Power States
•
If 𝑓
𝑂𝑁
= 𝑓
𝑂𝐹𝐹
, then 𝐸
𝑁
= 𝑆
𝑁
•
Should only happen if power management settings set incorrectly or poorly
•
Optimization only way to increase energy efficiency