9306

Automatic Compilation for Heterogeneous Architectures with Single Assignment C

Miguel Sousa Diogo
University of Amsterdam
University of Amsterdam, 2012
@article{diogo2012automatic,

   title={Automatic Compilation for Heterogeneous Architectures with Single Assignment C},

   author={Diogo, Miguel Sousa},

   year={2012}

}

Download Download (PDF)   View View   Source Source   

368

views

In recent years, we have witnessed an increasing heterogeneity of computing resources. A typical laptop today combines at least one multicore processor with one general purpose graphics processing unit (GPGPU), while supercomputer nodes typically have several of each. Exploiting all these available computing resources effectively is very important, but also still very challenging. In this work, we developed a system to automatically parallelise code for heterogeneous computing, using both multicore-CPUs and one or several GPGPUs in parallel, based on the Single Assignment C (SaC) compiler. SaC is a functional array programming language whose compiler can perform parallelisation automatically in a data-parallel approach. The data-parallel nature of SaC programs makes them good matches for regular multicore processors and GPGPU accelerators. Both architectures are supported by the SaC compiler, yet they can’t be used simultaneously. At compile time, a choice must be made between either ignoring the GPGPUs and using the multiple cores, or using a single GPGPU with one host system core. We present in this work an extension to the compilation process of SaC to allow multiple target architectures, combining both existing code generation alternatives, without support from the programmer. We also present a corresponding runtime system to manage the multiple memories involved in the heterogeneous computing resources. We finally evaluate our work using several benchmarks, showing significant performance improvements by using heterogeneous computing.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Automatic Compilation for Heterogeneous Architectures with Single Assignment C, 5.0 out of 5 based on 1 rating

* * *

* * *

Like us on Facebook

HGPU group

168 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1275 peoples are following HGPU @twitter

* * *

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: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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 © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: