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
2. Model 3 Model 2 Model 1
3. What Why How
4. Parallelism: Boosting performance by executing at least two threads at the same time.
5. RayHightower.com
8. Why parallelism? Because Moore’s Law is running out.
9. Transistors on a wafer of silicon.
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16. Limited by Physics 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 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 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? 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17. Parallelism
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.
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 ======>
20. How?
21. Embarrassingly parallel problem.
22. Embarrassingly parallel problem. … like finding prime numbers.
26. ssh-keygen
27. authorized_keys
28. RPi01 RPi02 RPi03 RPi04 authorized_keys
30. The cluster works!
33. RPi Summary •Assemble the cluster. •Communicate via mpi4py. •Assign a portion of the problem to each node. •Gather the results.
34. What’s next?
35. Graphical Processing Unit
36. MacBook Pro Video 384 cores
37. Raspberry Pi is a gateway for the GPU.
44. Mapping complicated algorithms to massively parallel hardware architectures is considered a non-trivial problem. - Andreas Olofsson, CEO
 Adapteva
45. Mapping complicated algorithms to massively parallel hardware architectures is considered a non-trivial problem. - Andreas Olofsson, CEO
 Adapteva It’s hard.
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.
47. Moore’s Law: 2x every 18 months
48. Moore’s Law for GPUs https://www.nextbigfuture.com/2017/04/for-commoners-using-cpus-meaningful.html Moore Nvidia
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
50. Nvidia Jetson Nano 128 GPU cores 4 CPU cores
51. Nvidia Jetson Nano 128 GPU cores 4 CPU cores $99
52. Nvidia Jetson Nano 128 GPU cores 4 CPU cores $99
54. Compute Unified Device Architecture
55. CUDA supports Python, Clojure, C, C++, C#, Fortran, Ruby, Java…
58. Embarrassingly parallel problem. … deep learning.
61. •Training •Testing •Deployment
62. Avoiding “gotchas” •Lots of data/examples •Labeling. •Randomly choose 20% for testing. •Don’t train w/testing data.
63. Parallelism accelerates deep learning.
67. Resources
69. https://www.youtube.com/watch?v=aircAruvnKk&list=PLOnWKC1gI_OOIrkRZDq2SgYoDxfRAJtdF&index=66&t=0s Recommended by the Nvidia instructors.
70. http://alexlenail.me/NN-SVG/index.html
75. Summary… •Parallelism + RPi •Jetson Nano •Deep Learning •Resources
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
77. Thanks! RayHightower.com