@RayHightower
All Things Open
October 13-15, 2019
Raleigh, NC
USA
Raspberry Pi is a
Gateway Drug
for GPUs
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What
Why
How
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Parallelism:
Boosting performance
by executing
at least two threads
at the same time.
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RayHightower.com
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Why parallelism?
Because
Moore’s Law
is running out.
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Transistors
on a wafer
of silicon.
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Limited by
Eyesight
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Limited by
Physics
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Parallelism
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Concurrency
At least two
threads are
making
progress.
Parallelism
At least two
threads are
executing
simultaneously.
Oracle Multithreaded Programming Guide
http://docs.oracle.com/cd/E19455-01/806-5257/6je9h032b/index.html
vs.
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Parallelism
Keyboard
Mouse
Disk
Time ======>
Concurrency
Task
Switching
K M D K M D K M D K M D K M D K M D
Time ======>
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How?
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Embarrassingly
parallel problem.
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Embarrassingly
parallel problem.
… like finding
prime numbers.
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Model 3
Model 2
Model 1
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ssh-keygen
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authorized_keys
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RPi01
RPi02
RPi03
RPi04
authorized_keys
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The cluster
works!
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RPi Summary
•Assemble the cluster.
•Communicate via mpi4py.
•Assign a portion of the
problem to each node.
•Gather the results.
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What’s
next?
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Quick detour…
Parallella
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If one ox could not do the job
they did not try to grow a
bigger ox, but used two oxen.
When we need greater
computer power, the answer
is not to get a bigger
computer, but to build
systems of computers and
operate them in parallel.
-Grace Hopper
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RJ-45
Power
μUSB
μHDMI
μSD
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Summary: Finding Primes
0" 50" 100" 150" 200" 250"
Parallel
Parallella
Serial
Mac
Serial
Parallella
18.6 sec
14.4 sec
237.1 sec
($2,000.00 Apple MacBook Pro)
($150.00 Parallella)
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What’s
next?
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Graphical
Processing
Unit
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MacBook Pro Video
384 cores
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Raspberry Pi
is a gateway
for the GPU.
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Mapping complicated
algorithms to massively
parallel hardware
architectures is considered a
non-trivial problem.
- Andreas Olofsson, CEO
Adapteva
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Mapping complicated
algorithms to massively
parallel hardware
architectures is considered a
non-trivial problem.
- Andreas Olofsson, CEO
Adapteva
It’s hard.
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Mapping complicated
algorithms to massively
parallel hardware
architectures is considered a
non-trivial problem.
- Andreas Olofsson, CEO
Adapteva
It’s hard.
And it’s
worth it.
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Moore’s Law:
2x every 18 months
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Moore’s Law for GPUs
https://www.nextbigfuture.com/2017/04/for-commoners-using-cpus-meaningful.html
Moore
Nvidia
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# of Nodes Total Render Time Power Cost (5yrs) System Cost
CPU - Dual Skylake 25 38 hours $70k $250k
Nvidia RTX Server 1 6 hours $10k $30k
“Pixar artists already rely on Nvidia ray tracing, and RTX more than doubles the performance
they will see. We’re excited to use RTX on our upcoming films.”
-Steve May, CTO, Pixar
From Jensen Huang’s keynote at Nvidia GTC 2019
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Nvidia Jetson Nano
128 GPU cores
4 CPU cores
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Compute
Unified
Device
Architecture
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CUDA supports Python,
Clojure, C, C++, C#,
Fortran, Ruby, Java…
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Embarrassingly
parallel problem.
… deep learning.
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•Training
•Testing
•Deployment
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Avoiding “gotchas”
•Lots of data/examples
•Labeling.
•Randomly choose 20% for testing.
•Don’t train w/testing data.
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Parallelism
accelerates
deep learning.
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Resources
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https://www.youtube.com/watch?v=aircAruvnKk&list=PLOnWKC1gI_OOIrkRZDq2SgYoDxfRAJtdF&index=66&t=0s
Recommended
by the Nvidia
instructors.
In business, there is nothing
more valuable than a
technical advantage your
competitors don’t
understand.
-Paul Graham
http://www.paulgraham.com/avg.html