Opendda: a Novel High-Performance Computational Framework for the Discrete Dipole Approximation

TL;DR

OpenDDA introduces a highly optimized, parallel computational framework for the Discrete Dipole Approximation, significantly reducing computational complexity and memory usage while enhancing performance for optical property simulations.

Contribution

The paper presents novel optimizations including a new DFT kernel and parallel implementations that improve efficiency and scalability of the DDA method.

Findings

60% reduction in 1-D transforms needed

Over 80% memory savings

Excellent performance and scalability in benchmarks

Abstract

This work presents a highly optimized computational framework for the Discrete Dipole Approximation, a numerical method for calculating the optical properties associated with a target of arbitrary geometry that is widely used in atmospheric, astrophysical and industrial simulations. Core optimizations include the bit-fielding of integer data and iterative methods that complement a new Discrete Fourier Transform (DFT) kernel, which efficiently calculates the matrix vector products required by these iterative solution schemes. The new kernel performs the requisite 3-D DFTs as ensembles of 1-D transforms, and by doing so, is able to reduce the number of constituent 1-D transforms by 60% and the memory by over 80%. The optimizations also facilitate the use of parallel techniques to further enhance the performance. Complete OpenMP-based shared-memory and MPI-based distributed-memory implementations have been created to take full advantage of the various architectures. Several benchmarks of the new framework indicate extremely favorable performance and scalability. OpenDDA is available following the usual open source regulations from http://www.opendda.org