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NVIDIA Confidential 
Exascale 
An Innovator’s Dilemma 
Jen-Hsun Huang, CEO 
SC11, Seattle, Washington | Nov. 15, 2011
"Generally, disruptive innovations were 
technologically straightforward, 
consisting of off-the-shelf components 
put together in a product architecture 
that was often simpler than prior 
approaches. They offered less of what 
customers in established markets 
wanted and so could rarely be initially 
employed there. They offered a 
different package of attributes valued 
only in emerging markets remote from, 
and unimportant to, the mainstream.”
NVIDIA Confidential
A History of Amazing Advances 
1988 1998 2009 
ZETTAFLOPS 
EXAFLOPS 
PETAFLOPS 
TERAFLOPS 
GIGAFLOPS 
Cray T3E-1200 
0.9 TF 
Cray XT-5 
“Jaguar” 
1.8 PF, 7 MW 
Cray Y-MP8 
2.7 GF, 0.15 MW
Advances with Dennard Scaling 
1988 1998 2009 
ZETTAFLOPS 
EXAFLOPS 
PETAFLOPS 
TERAFLOPS 
GIGAFLOPS 
Cray T3E-1200 
0.9 TF 
Cray XT-5 
“Jaguar” 
1.8 PF, 7 MW 
Cray Y-MP8 
2.7 GF, 0.15 MW 
½ L every 4 yrs 
8X – same P 
1.68X per/W CAGR
End of Dennard Scaling 
1988 1998 2009 
ZETTAFLOPS 
EXAFLOPS 
PETAFLOPS 
TERAFLOPS 
GIGAFLOPS 
Cray T3E-1200 
0.9 TF 
Cray XT-5 
“Jaguar” 
1.8 PF, 7 MW 
Cray Y-MP8 
2.7 GF, 0.15 MW 
½ L every 4 yrs 
8X @ 4X P 
1.19X per/W CAGR
Supercomputing is Power Limited 
1988 1998 2009 
ZETTAFLOPS 
PETAFLOPS 
TERAFLOPS 
GIGAFLOPS 
100 PF, 20 MW 
2012 2035 
EXAFLOPS 
Cray T3E-1200 
0.9 TF 
Cray XT-5 
“Jaguar” 
1.8 PF, 7 MW 
Cray Y-MP8 
2.7 GF, 0.15 MW 
70 PF, 20 MW 
1 EF, 20 MW 
CPU-only 
“Titan” 
6 PF, 8.6 MW 
2019 2022
CPUs Fast But Complex 
Optimized for single-threaded 
performance 
~50X energy to schedule 
instruction than the operation 
~20X energy to move data across 
chip than the calculation
Super Efficient Processors Needed 
Many simple processors with 
minimal overhead 
Locality reduces data movement 
energy 
Poor single-threaded performance 
Innovator’s Dilemma!
PRINCIPLE #1 
Companies depend on customers 
and investors for resources. 
Clayton M. Christensen 
(1997) The Innovator’s Dilemma: 
When New Technologies Cause Great Firms to Fail
PRINCIPLE #2 
Small markets don’t solve growth 
needs of large companies. 
Clayton M. Christensen 
(1997) The Innovator’s Dilemma: 
When New Technologies Cause Great Firms to Fail
GPU Computing 
“New Market” Disruption
Disruptive technologies underperform 
established products in mainstream markets. 
Cheaper, smaller, and frequently more 
convenient. 
Clayton M. Christensen 
(1997) The Innovator’s Dilemma: 
When New Technologies Cause Great Firms to Fail
Nagasaki University 
Professor Tsuyoshi Hamada’s 
Homemade supercomputer
GPU Computing 
>350,000,000 GPUs with CUDA 
>1,000,000 Toolkit Downloads 
>120,000 Active Developers 
>475 Universities Teaching GPU Computing 
100% HPC OEMs offer GPU Clusters
World’s First 
Whole H1N1 Virus Simulation
Lifecycles of fish 
in Australia 
University of Melbourne 
Stars and galaxies 
12.5B years ago 
University of Groningen 
Neural networks in a 
self-learning robot 
The University of Plymouth 
Directives 
65x in 2 Days 5.6x in 5 Days 4.7x in 4 Hours
The Road to Exascale 
70 PF, 20 MW 
100 PF, 20 MW 
GPU-Accelerated 
“Titan” 
20 PF, 8.6 MW 
2012 2022 
PETAFLOPS 
1 EF, 20 MW 
2019 2035 
CPU-only 
“Titan” 
6 PF, 8.6 MW 
1 EF, 20 MW 
ZETTAFLOPS
GPU-Accelerated 
“Titan” 
20 PF, 8.6 MW 
2012 
ZETTAFLOPS 
PETAFLOPS 
2035 
The Road to Exascale 
70 PF, 20 MW 
100 PF, 20 MW 
1 EF, 20 MW 
CPU-only 
“Titan” 
6 PF, 8.6 MW 
1 EF, 20 MW 
1 EF, 20 MW 
2019 2022
GPU-Accelerated 1 EF, 20 MW 
2012 
ZETTAFLOPS 
PETAFLOPS 
2035 
The Road to Exascale 
70 PF, 20 MW 
100 PF, 20 MW 
“Titan” 
20 PF, 8.6 MW 
1 EF, 20 MW 
100 EF, 20 MW 
2019 2022 
CPU-only 
“Titan” 
6 PF, 8.6 MW
Huge Markets Support GPU 
Workstation 
5M 
Console 
20M 
PCs 
400M 
Mobile 
1,000M
“Teraflops” 5 Watts 
ASCI Red @ Sandia Labs 
1997 2019
DEMO: BATTLEFIELD 3
“Tens” of Teraflops 100 Watts 
Red Storm @ Sandia Labs 
2004 2019
DEMO: ASSASSIN’S CREED
1000 Watts 
“Hundreds” of Teraflops 
Blue Gene @ LLNL 
2006 2019
DEMO: MAYA
DEMO: FUMEFX
One Disruption Enables Another
‘Super’ Computing 
From Super Computers to Super Phones

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SC11 Jen-Hsun Huang Keynote

  • 1. NVIDIA Confidential Exascale An Innovator’s Dilemma Jen-Hsun Huang, CEO SC11, Seattle, Washington | Nov. 15, 2011
  • 2. "Generally, disruptive innovations were technologically straightforward, consisting of off-the-shelf components put together in a product architecture that was often simpler than prior approaches. They offered less of what customers in established markets wanted and so could rarely be initially employed there. They offered a different package of attributes valued only in emerging markets remote from, and unimportant to, the mainstream.”
  • 4. A History of Amazing Advances 1988 1998 2009 ZETTAFLOPS EXAFLOPS PETAFLOPS TERAFLOPS GIGAFLOPS Cray T3E-1200 0.9 TF Cray XT-5 “Jaguar” 1.8 PF, 7 MW Cray Y-MP8 2.7 GF, 0.15 MW
  • 5. Advances with Dennard Scaling 1988 1998 2009 ZETTAFLOPS EXAFLOPS PETAFLOPS TERAFLOPS GIGAFLOPS Cray T3E-1200 0.9 TF Cray XT-5 “Jaguar” 1.8 PF, 7 MW Cray Y-MP8 2.7 GF, 0.15 MW ½ L every 4 yrs 8X – same P 1.68X per/W CAGR
  • 6. End of Dennard Scaling 1988 1998 2009 ZETTAFLOPS EXAFLOPS PETAFLOPS TERAFLOPS GIGAFLOPS Cray T3E-1200 0.9 TF Cray XT-5 “Jaguar” 1.8 PF, 7 MW Cray Y-MP8 2.7 GF, 0.15 MW ½ L every 4 yrs 8X @ 4X P 1.19X per/W CAGR
  • 7. Supercomputing is Power Limited 1988 1998 2009 ZETTAFLOPS PETAFLOPS TERAFLOPS GIGAFLOPS 100 PF, 20 MW 2012 2035 EXAFLOPS Cray T3E-1200 0.9 TF Cray XT-5 “Jaguar” 1.8 PF, 7 MW Cray Y-MP8 2.7 GF, 0.15 MW 70 PF, 20 MW 1 EF, 20 MW CPU-only “Titan” 6 PF, 8.6 MW 2019 2022
  • 8. CPUs Fast But Complex Optimized for single-threaded performance ~50X energy to schedule instruction than the operation ~20X energy to move data across chip than the calculation
  • 9. Super Efficient Processors Needed Many simple processors with minimal overhead Locality reduces data movement energy Poor single-threaded performance Innovator’s Dilemma!
  • 10. PRINCIPLE #1 Companies depend on customers and investors for resources. Clayton M. Christensen (1997) The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail
  • 11. PRINCIPLE #2 Small markets don’t solve growth needs of large companies. Clayton M. Christensen (1997) The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail
  • 12.
  • 13. GPU Computing “New Market” Disruption
  • 14. Disruptive technologies underperform established products in mainstream markets. Cheaper, smaller, and frequently more convenient. Clayton M. Christensen (1997) The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail
  • 15. Nagasaki University Professor Tsuyoshi Hamada’s Homemade supercomputer
  • 16. GPU Computing >350,000,000 GPUs with CUDA >1,000,000 Toolkit Downloads >120,000 Active Developers >475 Universities Teaching GPU Computing 100% HPC OEMs offer GPU Clusters
  • 17. World’s First Whole H1N1 Virus Simulation
  • 18. Lifecycles of fish in Australia University of Melbourne Stars and galaxies 12.5B years ago University of Groningen Neural networks in a self-learning robot The University of Plymouth Directives 65x in 2 Days 5.6x in 5 Days 4.7x in 4 Hours
  • 19.
  • 20.
  • 21. The Road to Exascale 70 PF, 20 MW 100 PF, 20 MW GPU-Accelerated “Titan” 20 PF, 8.6 MW 2012 2022 PETAFLOPS 1 EF, 20 MW 2019 2035 CPU-only “Titan” 6 PF, 8.6 MW 1 EF, 20 MW ZETTAFLOPS
  • 22. GPU-Accelerated “Titan” 20 PF, 8.6 MW 2012 ZETTAFLOPS PETAFLOPS 2035 The Road to Exascale 70 PF, 20 MW 100 PF, 20 MW 1 EF, 20 MW CPU-only “Titan” 6 PF, 8.6 MW 1 EF, 20 MW 1 EF, 20 MW 2019 2022
  • 23. GPU-Accelerated 1 EF, 20 MW 2012 ZETTAFLOPS PETAFLOPS 2035 The Road to Exascale 70 PF, 20 MW 100 PF, 20 MW “Titan” 20 PF, 8.6 MW 1 EF, 20 MW 100 EF, 20 MW 2019 2022 CPU-only “Titan” 6 PF, 8.6 MW
  • 24. Huge Markets Support GPU Workstation 5M Console 20M PCs 400M Mobile 1,000M
  • 25. “Teraflops” 5 Watts ASCI Red @ Sandia Labs 1997 2019
  • 27. “Tens” of Teraflops 100 Watts Red Storm @ Sandia Labs 2004 2019
  • 29. 1000 Watts “Hundreds” of Teraflops Blue Gene @ LLNL 2006 2019
  • 33.
  • 34.
  • 35. ‘Super’ Computing From Super Computers to Super Phones