Exploring GPGPUs Workload Characteristics and Power Consumption

Sohan Lal, Jan Lucas, Mauricio Alvarez-Mesa, Ahmed Elhossini, Ben Juurlink
Department of Embedded Systems Architecture, Technical University of Berlin, Einsteinufer 17, D-10587 Berlin, Germany
Technical University of Berlin, 2013

   title={Exploring GPGPUs Workload Characteristics and Power Consumption},

   author={Lal, Sohan and Lucas, Jan and Alvarez-Mesa, Mauricio and Elhossini, Ahmed and Juurlink, Ben},



Download Download (PDF)   View View   Source Source   



While general purpose computing on GPUs continues to enjoy higher computing performance with every new generation. The high power consumption of GPUs is an increasingly important concern. To create power-efficient GPUs, it is important to thoroughly study its power consumption. The power consumption of GPUs varies significantly with workloads. Therefore, in this work we study GPU power consumption at a detailed level and its correlation with well-known workload characteristics such as IPC. The low IPC kernels are further explored for the possible bottlenecks.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1542 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

274 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: