9595

GPU-Optimized Hybrid Neighbor/Cell List Algorithm for Coarse-Grained Molecular Dynamics Simulations

Andrew J. Proctor
Wake Forest University, Graduate School of Arts and Sciences
Wake Forest University, 2013
@phdthesis{proctor2013gpu,

   title={GPU-Optimized Hybrid Neighbor/Cell List Algorithm for Coarse-Grained Molecular Dynamics Simulations},

   author={PROCTOR, ANDREW J},

   year={2013},

   school={Wake Forest University}

}

Download Download (PDF)   View View   Source Source   

369

views

Molecular Dynamics (MD) simulations provide a molecular-resolution picture of the folding and assembly processes of biomolecules, however, the size and timescales of MD simulations are limited by the computational demands of the underlying numerical algorithms. Recently, Graphics Processing Units(GPUs), specialized devices that were originally designed for rendering images, have been repurposed for high performance computing with significant increases in performance for parallel algorithms. In this thesis, we briefly review the history of high performance computing and present the motivation for recasting molecular dynamics algorithms to be optimized for the GPU. We discuss cutoff methods used in MD simulations including the Verlet Neighbor List algorithm, Cell List algorithm, and a recently developed GPU-optimized parallel Verlet Neighbor List algorithm implemented in our simulation code, and we present performance analyses of the algorithm on the GPU. There exists an N-dependent speedup over the CPU-optimized version that is ~30x faster for the full 70s ribosome (N=10,219 beads). We then implement our simulations into HOOMD, a leading general particle dynamics simulation code that is also optimized for GPUs. Our simulation code is slower for systems less than around 400 beads but is faster for systems greater than 400 beads up to about 1,000 beads. After that point, HOOMD is unable to accommodate any more beads, but our simulation code is able to handle systems much larger than 10,000 beads. We then introduce a GPU-optimized parallel Hybrid Neighbor/Cell List algorithm. From our performance benchmark analyses, we observe that it is ~10% faster for the full 70s ribosome than our implementation of the the parallel Verlet Neighbor List algorithm.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

149 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1236 peoples are following HGPU @twitter

Featured events

* * *

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: