Parallel GPGPU Evaluation of Small Angle X-ray Scattering Profiles in a Markov Chain Monte Carlo Framework

TY - CHAP

T1 - Parallel GPGPU Evaluation of Small Angle X-ray Scattering Profiles in a Markov Chain Monte Carlo Framework

AU - Antonov, Lubomir Dimitrov

AU - Andreetta, Christian

AU - Hamelryck, Thomas Wim

N1 - Conference code: 5

PY - 2013

Y1 - 2013

N2 - Inference of protein structure from experimental data is of crucial interest in science, medicine and biotechnology. Low-resolution methods, such as small angle X-ray scattering (SAXS), play a major role in investigating important biological questions regarding the structure of proteins in solution.To infer protein structure from SAXS data, it is necessary to calculate the expected experimental observations given a protein structure, by making use of a so-called forward model. This calculation needs to be performed many times during a conformational search. Therefore, computational efficiency directly determines the complexity of the systems that can be explored.We present an efficient implementation of the forward model for SAXS with full hardware utilization of Graphics Processor Units (GPUs). The proposed algorithm is orders of magnitude faster than an efficient CPU implementation, and implements a caching procedure employed in the partial forward model evaluations within a Markov chain Monte Carlo framework.

AB - Inference of protein structure from experimental data is of crucial interest in science, medicine and biotechnology. Low-resolution methods, such as small angle X-ray scattering (SAXS), play a major role in investigating important biological questions regarding the structure of proteins in solution.To infer protein structure from SAXS data, it is necessary to calculate the expected experimental observations given a protein structure, by making use of a so-called forward model. This calculation needs to be performed many times during a conformational search. Therefore, computational efficiency directly determines the complexity of the systems that can be explored.We present an efficient implementation of the forward model for SAXS with full hardware utilization of Graphics Processor Units (GPUs). The proposed algorithm is orders of magnitude faster than an efficient CPU implementation, and implements a caching procedure employed in the partial forward model evaluations within a Markov chain Monte Carlo framework.

U2 - 10.1007/978-3-642-38256-7_15

DO - 10.1007/978-3-642-38256-7_15

M3 - Book chapter

SN - 978-3-642-38255-0

VL - 357

T3 - Communications in Computer and Information Science

SP - 222

EP - 235

BT - Biomedical Engineering Systems and Technologies

A2 - Gabriel, Joaquim

A2 - Schier, Jan

A2 - Huffel, Sabine Van

PB - Springer Science+Business Media

T2 - BIOSTEC 2012

Y2 - 2 February 2012 through 5 February 2012

ER -