Your browser doesn't support javascript.
loading
GPU-accelerated multitiered iterative phasing algorithm for fluctuation X-ray scattering.
Kommera, Pranay Reddy; Ramakrishnaiah, Vinay; Sweeney, Christine; Donatelli, Jeffrey; Zwart, Petrus H.
Afiliação
  • Kommera PR; Applied Computer Science, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
  • Ramakrishnaiah V; Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA.
  • Sweeney C; Applied Computer Science, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
  • Donatelli J; Applied Computer Science, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
  • Zwart PH; Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
J Appl Crystallogr ; 54(Pt 4): 1179-1188, 2021 Aug 01.
Article em En | MEDLINE | ID: mdl-34429723
ABSTRACT
The multitiered iterative phasing (MTIP) algorithm is used to determine the biological structures of macromolecules from fluctuation scattering data. It is an iterative algorithm that reconstructs the electron density of the sample by matching the computed fluctuation X-ray scattering data to the external observations, and by simultaneously enforcing constraints in real and Fourier space. This paper presents the first ever MTIP algorithm acceleration efforts on contemporary graphics processing units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to accelerate the MTIP algorithm on NVIDIA GPUs. The computational performance of the CUDA-based MTIP algorithm implementation outperforms the CPU-based version by an order of magnitude. Furthermore, the Heterogeneous-Compute Interface for Portability (HIP) runtime APIs are used to demonstrate portability by accelerating the MTIP algorithm across NVIDIA and AMD GPUs.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article