A brief technical overview about GPU power consumption and performance, with references to the latest architecture developed by Nvidia: Maxwell and Tegra X1. Co-Author: Pietro Piscione (https://www.linkedin.com/pub/pietro-piscione/84/b37/926)

1. Power Consumption and
Performance trends on GPUs
Computer Architecture
Authors:
Piscione Pietro
Villardita Alessio
A.Y. 2014/2015
Degree: Computer Engineering

2. Nowadays
focus
...and CPU
CUDA Performance
comparison
Power consumption
and performance
Architecture
overview
Purposes
and
application
Mobile
devices
GPU
Benchmark

3. Why GPU ?
● Multimedia and
general purpose
applications
and recently, also
● High Performance
Computing applications,
"autonomous machines"
and automotive
CAD and
3D apps
Office
and PDF
readers
Multimedia
Audio/Video
Browsers

4. Power Consumption Overview
Domain
GPU power
consumption
Desktop and Workstation
(no mobility) 300W (50%)
Notebook
(mobility)
50W (71%)
And the smartphones ?

5. Power Consumption Overview: smartphone
Gaming 3D Rendering
Power(mW)

6. GPU power consumption on mobile
How much power
does a mobile
application require
in order to execute?
Why is the GPU so
“energy-hungry”?
The answer is in the
GPU Architecture.

7. Power Consumption vs Performance:
SPECInt benchmark

8. Nowadays
focus
...and CPU
CUDA Performance
comparison
Power consumption
and performance
Architecture
overview
Purposes and
application
Mobile
devices
GPU
Benchmark

9. GPU Architecture - Overview
● CUDA cores (up to
thousands)
● Streaming Multiprocessor
(SMM)
● Global memory
● Giga Thread (Scheduler)
● Cache (L1 and L2)

10. GPU Architecture - (SMM)
● Cuda Cores
● SFU
● LDST units
● Dispatcher unit
● Warp scheduler

11. What is CUDA ?
(Computer Unified Device Architecture)
Multithreading on multiple simple cores
It implies an higher throughtput than CPU

12. GPU and CPU communication

13. CUDA study case: quick sort

14. GPU vs CPU
GPU
● hundreds of simpler cores
CPU
● few very complex cores
● thousand of concurrent
hardware threads
● single-thread performance
optimization
● maximize floating-point
throughput
● transistor space dedicated
to complex ILP
● most die surface for
integer and fp units
● few die surface for integer
and fp units

15. Performance Comparison: computation power

16. Performance Comparison: throughput

17. Nowadays
focus
...and CPU
CUDA Performance
comparison
Power consumption
and performance
Architecture
overview
Purposes
and
application
Mobile
devices
GPU
Benchmark

18. Benchmark study case: Tegra X1
Main features:
● GPU Maxwell 256 core
● 20nm tecnology
● CPU Octacore
One Teraflop of
computation power.

19. GFXBenchmark description
GFXBenchmark metrics:
● Graphics performance
● Long-term performance stability
● Render quality
● Power consumption
Composed by:
● Manhattan test
● T-Rex test
● Battery and stability test

20. GFXBenchmark description
Manhattan test:
● GPU-intensive OpenGL ES 3 test
● High detailed scenes (gaming field)
● Low effects level

21. GFXBenchmark description
T-Rex test:
● GPU-intensive OpenGL ES 2 test
● Low detailed scenes
● High effects level

22. GFXBenchmark description
Battery and Stability test:
Battery life
Performance stability
How it’s made ?
Logging (FPS)

23. Tegra X1 on GFXBenchmark

24. Tegra X1 vs K1: power consumption
Architectural solutions to
improve energy efficiency:
● big.LITTLE processing
design
● Using of cluster migration
● Cache coherence solution
DRAM Energy efficiency

25. Conclusions
Power consumption reduction: current and future trends
involve both Hardware AND Software design
● Reduce data movement
● Lots of local processing in parallel
● Efficient caching and memory usage
● Where data lives
● Where computation happens, how it is scheduled
Energy efficiency must now be key metric.