High Performance Computing using GPGPU’s

Omar Usman Khan
Politecnico di Torino
Politecnico di Torino, 2013

   title={High Performance Computing using GPGPU’s},

   author={Khan, Omar Usman},


   school={Politecnico di Torino}


Download Download (PDF)   View View   Source Source   Source codes Source codes




Computer based simulation software having a basis in numerical methods play a major role in research in the area of natural and physical sciences. These tools allow scientists to attempt problems that are too large to solve using analytical methods. But even these tools can fail to give solutions due to computational or storage limits. However, as the performance of computer hardware gets better and better, the computational limits can be also addressed. One such area of work is that of magnetic field modeling, which plays a crucial role in various fields of research, especially those related to nanotechnology. Due to remarkable advancements made in this field, magnetic modelling has developed new found interest, and rightly so. The most significant impact of this interest is perhaps felt in increasing areal densities for data storage devices which is projected to reach almost atomic scales. Computational limits, and subsequently their solutions based on hardware delivering high performance, are therefore a key component in research in this field. The scale of length and time plays a crucial role in observing magnetic phenomena, and as these scales are reduced, new behaviours can be observed. Coarser scales may be beneficial if modeling larger systems, but when working with sub-micron scales, a finer scale has to be selected. Doing so will project the proper magnetic behaviour of the materials, but will come with its share of problems. These will be addressed in this thesis. Simulations are usually configured before being started. The configuration is performed using scripting based methods which need to reflect the proper environmental conditions. For example, simulating multiple bodies with varying orientations, non-uniform geometries, bodies consisting of multiple layers with each layer having different properties, etc. will all need different configuration methods. A performance based solution would need to be optimized for each type of simulation. This may require re-structuring of different components of a simulator. This thesis is devoted to addressing such problems listed above with a focus on performance based solutions. The scope of the work has been limited to magnetostatic field calculations in particular because they consume the most time in the overall simulation. The scope has also been confined to regular structured rectangular meshes which are popular in major micromagnetic simulation software. Using regular meshes, magnetostatic field calculations can exploit a performance boost by using Fast Fourier Transforms. Therefore, fast FFT libraries using open standards will also be addressed in this thesis. In particular, this thesis will be based on the development process of open standards for magnetic field modeling. The major contribution in this regard includes an OpenCL specific FFT library for GPU’s and a GPU based magnetostatic field solver which is used as an extension to the OOMMF simulator. The thesis covers some novel numerical techniques that have been developed to target particular simulation configurations to obtain maximum performance
VN:F [1.9.22_1171]
Rating: 3.0/5 (1 vote cast)
High Performance Computing using GPGPU's, 3.0 out of 5 based on 1 rating

* * *

* * *

Follow us on Twitter

HGPU group

1584 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

299 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: