Efficient Parallel FFTs for Different Computational Models

We select the Fast Fourier Transform (FFT) to demonstrate a methodology for deriving the optimal parallel algorithm according to predetermined performance metrics, within a computational model. Following the vector space framework for parallel permutations, we provide a specification language to capture the algorithm, derive the optimal parallel FFT specification, compute the arithmetic, memory, communication and load{balance complexity metrics, apply the analytical performance evaluation to PRAM, LPRAM, BSP and LogP computational models, and compare with actual performance results.Engineering and Applied Science

First-principle molecular dynamics with ultrasoft pseudopotentials: parallel implementation and application to extended bio-inorganic system

We present a plane-wave ultrasoft pseudopotential implementation of first-principle molecular dynamics, which is well suited to model large molecular systems containing transition metal centers. We describe an efficient strategy for parallelization that includes special features to deal with the augmented charge in the contest of Vanderbilt's ultrasoft pseudopotentials. We also discuss a simple approach to model molecular systems with a net charge and/or large dipole/quadrupole moments. We present test applications to manganese and iron porphyrins representative of a large class of biologically relevant metallorganic systems. Our results show that accurate Density-Functional Theory calculations on systems with several hundred atoms are feasible with access to moderate computational resources.Comment: 29 pages, 4 Postscript figures, revtex

Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems

Accurately predicting the performance of coronagraphs and tolerancing optical surfaces for high-contrast imaging requires a detailed accounting of diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and far-field diffraction effects, such as the Talbot effect, are captured by plane-to-plane propagation using Fresnel and angular spectrum propagation. This approach requires a sequence of computationally intensive Fourier transforms and quadratic phase functions, which limit the design and aberration sensitivity parameter space which can be explored at high-fidelity in the course of coronagraph design. This study presents the results of optimizing the multi-surface propagation module of the open source Physical Optics Propagation in PYthon (POPPY) package. This optimization was performed by implementing and benchmarking Fourier transforms and array operations on graphics processing units, as well as optimizing multithreaded numerical calculations using the NumExpr python library where appropriate, to speed the end-to-end simulation of observatory and coronagraph optical systems. Using realistic systems, this study demonstrates a greater than five-fold decrease in wall-clock runtime over POPPY's previous implementation and describes opportunities for further improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure

Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects

We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs