13,851 research outputs found
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images
In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculated for individual images, BioEM in principle addresses the challenge of
working with highly dynamic or heterogeneous systems not easily handled in
traditional EM reconstruction. However, the calculation of these posteriors for
large numbers of particles and models is computationally demanding. Here we
present highly parallelized, GPU-accelerated computer software that performs
this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI
parallelization combined with both CPU and GPU computing. The resulting BioEM
software scales nearly ideally both on pure CPU and on CPU+GPU architectures,
thus enabling Bayesian analysis of tens of thousands of images in a reasonable
time. The general mathematical framework and robust algorithms are not limited
to cryo-electron microscopy but can be generalized for electron tomography and
other imaging experiments
A Hybrid Decomposition Parallel Implementation of the Car-Parrinello Method
We have developed a flexible hybrid decomposition parallel implementation of
the first-principles molecular dynamics algorithm of Car and Parrinello. The
code allows the problem to be decomposed either spatially, over the electronic
orbitals, or any combination of the two. Performance statistics for 32, 64, 128
and 512 Si atom runs on the Touchstone Delta and Intel Paragon parallel
supercomputers and comparison with the performance of an optimized code running
the smaller systems on the Cray Y-MP and C90 are presented.Comment: Accepted by Computer Physics Communications, latex, 34 pages without
figures, 15 figures available in PostScript form via WWW at
http://www-theory.chem.washington.edu/~wiggs/hyb_figures.htm
Non-power-of-Two FFTs: Exploring the Flexibility of the Montium TP
Coarse-grain reconfigurable architectures, like the Montium TP, have proven to be a very successful approach for low-power and high-performance computation of regular digital signal processing algorithms. This paper presents the implementation of a class of non-power-of-two FFTs to discover the limitations and Flexibility of the Montium TP for less regular algorithms. A non-power-of-two FFT is less regular compared to a traditional power-of-two FFT. The results of the implementation show the processing time, accuracy, energy consumption and Flexibility of the implementation
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