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.

Transcript

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. What
   Why
   How
   

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

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4. RayHightower.com
   

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

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8. Transistors
   on a wafer
   of silicon.
   

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    this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text to
    small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for u
    to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is this text too small for us to read? Is
    

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16. Parallelism
    

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17. 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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18. 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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19. How?
    

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20. Embarrassingly
    parallel problem.
    

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

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22. View Slide

23. Model 3
    Model 2
    Model 1
    

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24. View Slide

25. View Slide

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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29. View Slide

30. The cluster
    works!
    

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31. View Slide

32. View Slide

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. Quick detour…
    Parallella
    

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36. 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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37. RJ-45
    Power
    μUSB
    μHDMI
    μSD
    

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38. View Slide

39. View Slide

40. 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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41. What’s
    next?
    

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42. Graphical
    Processing
    Unit
    

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43. MacBook Pro Video
    384 cores
    

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44. Raspberry Pi
    is a gateway
    for the GPU.
    

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45. View Slide

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47. View Slide

48. View Slide

49. View Slide

50. View Slide

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

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52. 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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53. 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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54. Moore’s Law:
    2x every 18 months
    

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

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56. # 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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57. Nvidia Jetson Nano
    128 GPU cores
    4 CPU cores
    

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58. View Slide

59. Compute
    Unified
    Device
    Architecture
    

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

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61. View Slide

62. Embarrassingly
    parallel problem.
    … deep learning.
    

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63. View Slide

64. View Slide

65. •Training
    •Testing
    •Deployment
    

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

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67. Parallelism
    accelerates
    deep learning.
    

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68. View Slide

69. View Slide

70. View Slide

71. Resources
    

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72. View Slide

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

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

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75. View Slide

76. View Slide

77. View Slide

78. View Slide

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

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80. 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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81. Thanks!
    RayHightower.com
    

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