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
2. What Why How
3. Parallelism: Boosting performance by executing at least two threads at the same time.
4. RayHightower.com
7. Why parallelism? Because Moore’s Law is running out.
8. Transistors on a wafer of silicon.
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15. 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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16. Parallelism
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.
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 ======>
19. How?
20. Embarrassingly parallel problem.
21. Embarrassingly parallel problem. … like finding prime numbers.
23. Model 3 Model 2 Model 1
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. Quick detour… Parallella
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
37. RJ-45 Power μUSB μHDMI μSD
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)
41. What’s next?
42. Graphical Processing Unit
43. MacBook Pro Video 384 cores
44. Raspberry Pi is a gateway for the GPU.
51. Mapping complicated algorithms to massively parallel hardware architectures is considered a non-trivial problem. - Andreas Olofsson, CEO Adapteva
52. Mapping complicated algorithms to massively parallel hardware architectures is considered a non-trivial problem. - Andreas Olofsson, CEO Adapteva It’s hard.
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.
54. Moore’s Law: 2x every 18 months
55. Moore’s Law for GPUs https://www.nextbigfuture.com/2017/04/for-commoners-using-cpus-meaningful.html Moore Nvidia
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
57. Nvidia Jetson Nano 128 GPU cores 4 CPU cores
59. Compute Unified Device Architecture
60. CUDA supports Python, Clojure, C, C++, C#, Fortran, Ruby, Java…
62. Embarrassingly parallel problem. … deep learning.
65. •Training •Testing •Deployment
66. Avoiding “gotchas” •Lots of data/examples •Labeling. •Randomly choose 20% for testing. •Don’t train w/testing data.
67. Parallelism accelerates deep learning.
71. Resources
73. https://www.youtube.com/watch?v=aircAruvnKk&list=PLOnWKC1gI_OOIrkRZDq2SgYoDxfRAJtdF&index=66&t=0s Recommended by the Nvidia instructors.
74. http://alexlenail.me/NN-SVG/index.html
79. Summary… •Parallelism + RPi •Jetson Nano •Deep Learning •Resources
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
81. Thanks! RayHightower.com