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Raspberry Pi is a Gateway Drug for GPUs

Raspberry Pi is a Gateway Drug for GPUs

The free lunch provided by Moore’s Law is running out. Still, customers want more computing power, more performance. Parallelism (especially on GPUs) offers a way for software developers to deliver more. This talk offers an intro to parallelism on the Raspberry Pi with open source tools like Python. Finally, the presenter will show how the skills one uses to write parallel code on the Raspberry Pi can be adapted to GPUs. Stated another way: Raspberry Pi is a gateway drug for GPUs.

Ray Hightower

October 14, 2019
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  1. @RayHightower
    All Things Open
    October 13-15, 2019
    Raleigh, NC
    USA
    Raspberry Pi is a
    Gateway Drug
    for GPUs

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  2. Parallelism:
    Boosting performance
    by executing
    at least two threads
    at the same time.

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  3. RayHightower.com

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  4. Why parallelism?
    Because
    Moore’s Law
    is running out.

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  5. Transistors
    on a wafer
    of silicon.

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  13. 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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  14. 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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  15. Embarrassingly
    parallel problem.

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  16. Embarrassingly
    parallel problem.
    … like finding
    prime numbers.

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  17. Model 3
    Model 2
    Model 1

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  18. authorized_keys

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  19. RPi01
    RPi02
    RPi03
    RPi04
    authorized_keys

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  20. The cluster
    works!

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  21. RPi Summary
    •Assemble the cluster.
    •Communicate via mpi4py.
    •Assign a portion of the
    problem to each node.
    •Gather the results.

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  22. What’s
    next?

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  23. Quick detour…
    Parallella

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  24. 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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  25. RJ-45
    Power
    μUSB
    μHDMI
    μSD

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  26. 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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  27. What’s
    next?

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  28. Graphical
    Processing
    Unit

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  29. MacBook Pro Video
    384 cores

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  30. Raspberry Pi
    is a gateway
    for the GPU.

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  31. Mapping complicated
    algorithms to massively
    parallel hardware
    architectures is considered a
    non-trivial problem.
    - Andreas Olofsson, CEO
    Adapteva

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  32. 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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  33. 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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  34. Moore’s Law:
    2x every 18 months

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  35. Moore’s Law for GPUs
    https://www.nextbigfuture.com/2017/04/for-commoners-using-cpus-meaningful.html
    Moore
    Nvidia

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  36. # 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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  37. Nvidia Jetson Nano
    128 GPU cores
    4 CPU cores

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  38. Compute
    Unified
    Device
    Architecture

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  39. CUDA supports Python,
    Clojure, C, C++, C#,
    Fortran, Ruby, Java…

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  40. Embarrassingly
    parallel problem.
    … deep learning.

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  41. •Training
    •Testing
    •Deployment

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  42. Avoiding “gotchas”
    •Lots of data/examples
    •Labeling.
    •Randomly choose 20% for testing.
    •Don’t train w/testing data.

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  43. Parallelism
    accelerates
    deep learning.

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  44. https://www.youtube.com/watch?v=aircAruvnKk&list=PLOnWKC1gI_OOIrkRZDq2SgYoDxfRAJtdF&index=66&t=0s
    Recommended
    by the Nvidia
    instructors.

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  45. http://alexlenail.me/NN-SVG/index.html

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  46. Summary…
    •Parallelism + RPi
    •Jetson Nano
    •Deep Learning
    •Resources

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  47. In business, there is nothing
    more valuable than a
    technical advantage your
    competitors don’t
    understand.
    -Paul Graham
    http://www.paulgraham.com/avg.html

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  48. Thanks!
    RayHightower.com

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