green computing using gpu’s
TRANSCRIPT
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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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