GREEN COMPUTING USING GPU’s

8/3/2019 GREEN COMPUTING USING GPUs

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Presented by

1)Y.Navanaeeth Krishnan 1st sem CSE

2)Vipin Dwyedi 1st sem CSE


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The reach of Information technology is very wide.

From a hand held blackberry to a datacentre .Every

element provides us an element to change .The

effects of this change may be in the near future or ina couple of years

IT touches everyone's life today .Not even a single

day passes without us relying on a computer.

It is estimated that if the amount of power consumed

per person for his IT needs is reduced by 15% it is

equivalent to removing one car from the road


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In a data center the computers, storage devices andair conditioners work in an inefficient manner

Many server computers run at less than 30% of their

server capacity Usually algorithms are run faster in a computer on

the cost of cache memory. The umpteen number ofregisters and cache memories in a computer cause it

to consume a lot of power .This not only causes alarge amount of heat to be produced it increases theresistance of the electrical pathways leading to the

processor


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CPU GPU


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CPUs have a lower clock speed when compared to

GPUs.

CPUs require 100% usage of processing powers to

do a computationally intensive calculation.Gpu

remains idle during the execution of this calculation.

Therefore the power consumed by the computer =

Power consumed by CPU+Power consumed by

GPU


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GPUs can never be shut off or put in low power mode

while the Monitor is on ,but the CPU can be put into

low power state even when the monitor is on.

The GPU has a processing speed greater than that of aCPU because it executes a program in parallel.

Therefore computationally intensive parts carried out on

the GPU takes less time as well as energy .

Since the GPU has a very high processing capacity therewill be no change in its usage and IT WILL STILL

REMAIN IN THE IDLE MODE during the processing

of a computationally intensive program


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We have implemented the running of acomputationally intensive application(multiplication of 2 full matrices of 1024 order )

in OpenCl. We have used the GPU to perform the

computationally intensive part while the sequentialpart is done by the CPU.

Therefore by running the computationally intensiveparts on GPUs the CPU is in a low power modebecause its cache memory and registers are not usedat all .Thus saving power .


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2016 MATRIXMULTIPLICATION IN CPU

2016 MATRIXMULTIPLICATION IN GPU


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DEVICE MATRIX A MATRIX B TIME(S) Kernel time

(S)

CPU 1024 1024 2.220 1.908

GPU 1024 1024 1.529 0.413

CPU 2016 2016 6.804 6.514

GPU 2016 2016 2.036 0.639


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GPUs cant perform sequential operations like input

and output like a cpu .

GPUs generate a lot of heat

GPUs consume more power when both graphics

and calculations are required like games.


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GREEN COMPUTING USING GPU’s