A quick look at the current state of the art in the world of GPU computation

1. GPU
Computing
and
the Next Gen
Cloud
JAYPRASAD HEGDE
jayprasad_hegde AT
haasalum.berkeley.edu

2. Contents
• GPU Computing
• Applications
• Usage and Cloud Offerings
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3. Where are we headed?
• Plateau in performance improvements with Single Threaded Architecture
Image: Courtesy Nvidia GTC 2017 Keynote
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4. Picking up the slack
Image: Courtesy Nvidia GTC 2017 KeynoteJune 3, 2017 4

5. Key Trigger applications
• Bitcoin Mining
• AMD’s Radeon cards being used for mining on the cheap
• Temporary shortage of graphics cards
• Geoffrey Hinton, Univ of Toronto applying Neural Networks on an massive
scale using GPUs --- Origin of Deep Learning
• Limited adoption of NN until 2004. Invented in 1948 (Hebb, Rosenblatt).
• Popular Machine Learning method even prior to 2004. Small scale – CPU based
• Also, Kaggle winners in 2012 – NNs to run on GPUs using CUDA
• http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/
• https://arxiv.org/pdf/1702.07800.pdf (Origin of Deep Learning)
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6. Computation super-heavy applications
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7. Newer Super-duper computers
or super-farms
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8. CPU: Designed
for Low Latency
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9. GPU Architecture: Two Main Components
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10. GPU:
Designed for
Throughput
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16. Sense of proportions with a Kepler GK110 (2014)
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Each SMX has 2048 threads
15 x 2048 threads

17. Upcoming startups in Deep Learning
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18. Upcoming startups with Deep Learning
Higher level platforms
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19. Current State of the Art – Commodity
Hardware: Tesla V100
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Image: Courtesy Nvidia Announcement in GTC 2017

20. Frameworks performance for Volta
K80 is Kepler, P100 is Pascal and V100 is Volta
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21. For your home
• 4 way SLI bridge
• GTX 1080 Ti or Titan
Xp
• $10,000+
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22. For a mini setup
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$ 69,000

23. For the Datacentre
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Food for thought:
GTX 1080 in your Desktop will
do 8-10 TFLOPS

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25. Computation Frameworks, Cloud and GPU
systems
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27. Thank you!
• Image credits: Nvidia Keynote GTC 2017 and CSCSCH talk on CUDA by
Paul Bessmer
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