10533

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales
Texas Advanced Computing Center, The University of Texas at Austin, J.J. Pickle Research Campus, Building 196, Austin, Texas
Extreme Scaling Workshop (XSCALE13), 2013
@article{rosales2013porting,

   title={Porting to the Intel Xeon Phi: Opportunities and Challenges},

   author={Rosales, C},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

896

views

This work describes the challenges presented by porting code to the Intel Xeon Phi coprocessor, as well as opportunities for optimization and tuning. We use micro-benchmarks, code segments, assembly listings and application level results to illustrate the key issues in porting to the Xeon Phi coprocessor, always keeping in mind both portability and performance. While executing code on the Xeon Phi in native mode is fairly straightforward it can be a challenge to achieve good performance. The complexity of optimization increases as one introduces offload, distributed offload, or symmetric execution modes. We will initially focus on the fundamental issues that can prevent acceptable performance in native execution, and then address the key issues in data transfers due to either offloaded regions or MPI exchanges with the host CPU. Some of the issues are of a generic nature and affect any code using heterogeneous execution – PCIe bandwidth bottleneck -, and others are specific to the Xeon Phi and its software environment – Host/MIC MPI exchanges. We will also make an effort to indicate which issues are specific to this platform and which are of general applicability. In particular we will draw comparisons between the data management models in the Intel Xeon Phi and in the NVIDIA CUDA environment.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

* * *

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 12.2
  • SDK: 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 © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: