Energy-aware ComputingSoftware approaches and other technologies

Name ID

Abd ElRahman Abd Elkawy 19-4735

Kareem Rezk 19-9237

Mohamed Elhawary 19-7157

Omar Elshal 19-8014

Content Layout:

• Why Energy-aware ?

• Energy and Environment (Green Computing)

• Power reduction Approaches

• Low power through parallelism

• Graphene, how can it contribute to Energy-aware?

• Graphene supercapacitor

• References

Why Energy-aware?

• Data centers consumed 61 billion kilowatt-hours (kWh) in 2006 (1.5% of total U.S. electricity consumption costing $4.5 billion)

• According to Koomey’s report (2011), only 56% increase through 2006-2011 due to virtualization

• 2011- $7.4 billion (25 power plants)

• But still the growth is exponential.

Source: T. Hoefler: Software and Hardware Techniques for Power-Efficient HPC Networking

Why Energy-aware?

• Processors are getting hotter

• Heat needs to be transferred away, or the chip dies:

• For every 10 degree Celsius increase in temperature, the lifetime of a chip reduces by half !

• Expensive solution (liquid cooling)

• Fans, but consume power too

Energy and Environment (Green Computing)

• It’s the study of designing, manufacturing, using and disposing of computers, servers and associated subsystems efficiently and effectively with minimal on environment.

• According to German Federal Environment office, computers consume around 17billion kWh each year in standby mode only !

• The CO2 dissipated from ‘sleeping devices’ = 1/7 the CO2 emitted from a car

Power reduction Approaches

• Algorithmic Level

• Complier Level

• Architecture Level

• Organization Level

• Circuit Level

Algorithmic level

• Fewer instructions/cycles reduces energy

• Trying alternative algorithms with lower complexity:

• E.g. quick sort O(nlogn) , bubble sort O(n^2)

• Heuristic approach, go for a good solution, but not the best

• Biggest gains at this level

Compiler level

• Strength reduction

• E.g. replace multiplications with Add’s and Shift’s

• E.g. replace floating point with fixed point

• Source-to-Source transformation

• Loop transformation to improve locality

• Reorder instructions to reduce bit-transition

• Reduce register pressure (number of accesses to register file)

• Perform special optimizations per scenario of each execution mode

Architecture, Organization level

• Going parallel

• Add local memories

For Organization level (micro Architecture)

• Reducing Vdd by using lower freq.

• Pipelining(cheap way of parallelism)

• Reducing register traffic

• Avoid unnecessary reads and writes

Circuit level

• Clock gating

• add more logic to circuit to prune clock tree

• Power gating

• shut off current to blocks not in use in circuit

• Use special SRAM cells

• Normal SRAM can’t scale below Vdd =0.7-0.8 Volt

• Multiple Vdd modes

Low power through parallelism

• Sequential Processor

• Switching capacitance C

• Frequency f

• Voltage V

• P1 = αfCV2

• Parallel Processor (two times the number of units)

• Switching capacitance 2C

• Frequency f/2

• Voltage V’ < V

• P2 = αf/2*2CV’2 = αfCV’2 < P1

Graphene & Energy-aware

• It’s a single layer of graphite (pure crystalline carbon)

• First isolated in lab in 2004 by Andre Geim and Konstantin Novoselov at the University of Manchester (won Nobel Prize in Physics in 2010)

• It is the thinnest material imaginable (~0.345 nm thick)

• It is electrically conductive – best known so far

• 1,000,000x more conductive than copper (current density at room temp.)

• Replacement for Solar cells, touchscreens, new computers, batteries, etc.

Graphene supercapacitor

• The graphene supercapacitor is capable of charging up to 1,000 times faster than a normal battery

• Fully charge your phone in 30 seconds and last for days

• Contains no toxic chemical, carbon based (Green)

• Ten grams of graphene is the same weight as two nickels

• These ten grams could cover the electricity of Cairo stadium

• 15 Kgs of graphene would cover all of the computer displays in the world

• 15 kgs of graphene is equivalent in weight to a standard cinder block

References

• http://htor.inf.ethz.ch/publications/img/hoefler-energy-utah.pdf

• http://www.slideshare.net/snehasispanigrahi/green-computing-9739418

• www.inf.ed.ac.uk/teaching/courses/eac/01_Intro.pdf

• http://www.ics.ele.tue.nl/~heco/courses/ASCI-winterschools/Energy-aware-computing-27mar2012.ppt

• http://cloudbus.org/papers/Energy-Aware-CloudResourceAllocation-FGCS2012.pdf

Questions ?