The Analysis of Functional Neuroimaging (AFNI) is a widely adopted software package in the fMRI data analysis community. For many types of analysis pipelines, one important step is to register a subject's image to a pre-defined template so different images can be compared within a normalized coordination system. Although a 12-point affine transformation works fine for some standard cases, it is usually found insufficient for voxelwise types of analyses. This is especially challenging if the subject has brain atrophy due to some kinds of neurological condition such as Parkinson's disease. The 3dQwarp code in AFNI is a non-linear image registration procedure that overcomes the drawbacks of a canonic affine transformation. However, the existing OpenMP instrumentation in 3dQwarp is not efficient for warping at an ultra fine level, and the constant trip counts of the iterative algorithm also hurts the accuracy. Based on the profiling and benchmark analysis, we improve the parallel efficiency by the optimization of its OpenMP structure and obtain about 2x speedup for normalized workload. With the incorporation of a convergence criteria, we are able to perform warping at a much finer level beyond the default threshold and achieve about 20% improvement in term of Pearson correlation.
MPAS (Model for Prediction Across Scales) Atmosphere is a highly scalable application for global weather and climate simulations. It uses an unstructured Voronoi mesh in the horizontal dimensions, thereby avoiding problems associated with traditional rectilinear grids, and deploys a subset of the atmospheric physics used in WRF. In this paper, we describe work that was done to improve the overall performance of the software: serial optimization of the dynamical core and thread-level load balancing of the atmospheric physics. While the overall reductions were modest for standard benchmarks, we expect that the contributions will become more important with the eventual addition of atmospheric chemistry or when running at larger scale.
The short iterative Lanczos method has been combined with the finite-element discrete variable representation to yield a powerful approach to solving the time-dependent Schroedinger equation. It has been applied to the interaction of short, intense laser radiation(attosecond pulses) to describe the single and double ionization of atoms and molecules, but the approach is not limited to this particular application. The algorithm will be described in some detail and how it been successfully ported to the Intel Phi coprocessors. While further experimentation is needed, the current results provide reasonable evidence that by suitably modifying the code to combine MPI, OpenMP, and compiler offload directives, one can achieve significant improvement in performance from these coprocessors for problems such as the above.
