Exploring Parallelism with Raspberry Pi and Nvidia Jetson Nano

The free lunch provided by Moore's Law is running out. Still, customers will always want more performance. Parallelism (especially with GPUs) clears a path for software developers to deliver more. This talk offers an exploration of parallelism and artificial intelligence on GPUs with open source tools like Python and Clojure. Audience members will learn how GPU acceleration can boost the performance of AI applications. And everyone will learn how to get up and running with an inexpensive 128-core GPU device, the Nvidia Jetson Nano.

Transcript

1. @RayHightower
   Nebraska.Code()
   August 14-16, 2019
   Lincoln, NE
   USA
   Exploring Parallelism
   w/Raspberry Pi
   & Jetson Nano
   

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2. Model 3
   Model 2
   Model 1
   

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3. What
   Why
   How
   

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

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5. RayHightower.com
   

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

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9. Transistors
   on a wafer
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17. Parallelism
    

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18. 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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19. 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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20. How?
    

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21. Embarrassingly
    parallel problem.
    

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

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26. ssh-keygen
    

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27. authorized_keys
    

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28. RPi01
    RPi02
    RPi03
    RPi04
    authorized_keys
    

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30. The cluster
    works!
    

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

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34. What’s
    next?
    

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35. Graphical
    Processing
    Unit
    

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36. MacBook Pro Video
    384 cores
    

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37. Raspberry Pi
    is a gateway
    for the GPU.
    

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

    Adapteva
    

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45. 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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46. 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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47. Moore’s Law:
    2x every 18 months
    

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

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49. # 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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50. Nvidia Jetson Nano
    128 GPU cores
    4 CPU cores
    

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51. Nvidia Jetson Nano
    128 GPU cores
    4 CPU cores
    $99
    

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52. Nvidia Jetson Nano
    128 GPU cores
    4 CPU cores
    $99
    

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53. View Slide

54. Compute
    Unified
    Device
    Architecture
    

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

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58. Embarrassingly
    parallel problem.
    … deep learning.
    

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60. View Slide

61. •Training
    •Testing
    •Deployment
    

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

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63. Parallelism
    accelerates
    deep learning.
    

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66. View Slide

67. Resources
    

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68. View Slide

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

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

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74. View Slide

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

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76. 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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77. Thanks!
    RayHightower.com
    

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