A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Shuaiwen Song, Chunyi Su, Barry Rountree, Kirk W. Cameron
Virginia Tech, Blacksburg, VA
27th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2013

   title={A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures},

   author={Song, Shuaiwen and Su, Chunyi and Rountree, Barry and Cameron, Kirk W.},



Download Download (PDF)   View View   Source Source   



Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine simplicity, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi). We combine hardware performance counter data with machine learning and advanced analytics to model power-performance efficiency for modern GPU-based systems. Our performance counter based approach is simpler than previous approaches and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture). We measure the accuracy of our power and performance models on a NVIDIA Fermi C2075 GPU for more than a dozen CUDA applications. We show our power model is more accurate and robust than the best available GPU power models – multiple linear regression models MLR and MLR+. We demonstrate how to use our models to identify power-performance bottlenecks and suggest optimization strategies for high-performance codes such as GEM, a biomolecular electrostatic analysis application. We verify our power-performance model is accurate on clusters of NVIDIA Fermi M2090s and useful for suggesting optimal runtime configurations on the Keeneland supercomputer at Georgia Tech.
VN:F [1.9.22_1171]
Rating: 5.0/5 (2 votes cast)
A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures, 5.0 out of 5 based on 2 ratings

* * *

* * *

Follow us on Twitter

HGPU group

1655 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: