Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Apan Qasem
Department of Computer Science, Texas State University, San Marcos, Texas, USA
American Journal of Computer Architecture, 1(2), 12-20, 2012

   title={Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels},

   author={Qasem, A.},

   journal={American Journal of Computer Architecture},





   publisher={Scientific & Academic Publishing}


Download Download (PDF)   View View   Source Source   



Hundreds of cores per chip and support for fine-grain multithreading have made GPUs a central player in todays HPC world. Much of the responsibility of achieving high performance on these complex systems lies with software like the compiler. This paper describes a compiler-based strategy for automatic and profitable application of the unroll-and-jam transformation to CUDA kernels. The framework supports specification of unroll factors through source-code annotation and also implements a heuristic based on register pressure and occupancy that recommends unroll factors for improved memory performance. We present experimental results on a GE 9800 GT on four CUDA kernels. The results show that the proposed strategy is generally able to select profitable unroll factors. The results also indicate that the selected unroll amounts strike the right balance between register pressure and occupancy.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1665 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 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: