We introduce the first AI-based framework for deep, super-resolution, wide-field radio interferometric imaging and demonstrate it on observations of the ESO 137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent “plug-and-play” scheme whereby a denoising operator is injected as an image regularizer in an optimization algorithm, which alternates until convergence between denoising steps and gradient-descent data fidelity steps. We investigate handcrafted and learned variants of high-resolution, high dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO 137-006 from 19 GB of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.
Memory and I/O performance bottlenecks in supercomputing simulations are two key challenges that must be addressed on the road to Exascale. The new byte-addressable persistent non-volatile memory technology from Intel, DCPMM, promises to be an exciting opportunity to break with the status quo, with unprecedented levels of capacity at near-DRAM speeds. Here, we explore the potential of DCPMM in the context of two high-performance scientific applications in terms of outright performance, efficiency and usability for both its Memory and App Direct modes. In Memory mode, we show equivalent performance and better efficiency for a CASTEP simulation that is limited by memory capacity on conventional DRAM-only systems without any changes to the application. For IFS, we demonstrate that a distributed object-store over NVRAM reduces the data contention created in weather forecasting data producer-consumer workflows. In addition, we also present the achievable memory bandwidth performance using STREAM.
This paper presents a complete methodology for performing finite-volume-based detached-eddy simulation for the prediction of aerodynamic forces and detailed flow structures of passenger vehicles developed using the open-source CFD toolbox OpenFOAM R. The main components of the methodology consist of an automatic mesh generator, a setup and initialisation utility, a DES flow solver and analysis and post-processing routines. Validation of the predictions is done on the basis of detailed comparisons to experimental wind-tunnel data. Results for lift and drag are found to compare favourably to the experiments, with some moderate discrepancies in predicted rear lift. Point surfacepressure measurements, oil-streak images and maps of total pressure in the flow field demonstrate the approach's capabilities to predict the fine detail of complex flow regimes found in automotive aerodynamics. Standard DES methods can cost an order of magnitude more than traditional methods, but optimisation and automation of mesh generation, setup and solution algorithms ensure quick turnaround times. Due to the fully parallel nature of these components, the entire process can be executed in a distributed fashion. Efficient solution algorithms provide exceptional accuracy when compared to Reynolds-averaged approaches without sacrificing stability, even when the flow exhibits high Courant numbers. The proposed methodology is highly customisable, which allows for targeted developments to suit the individual needs of aerodynamics CFD. On the basis of the results presented here, the methodology is found to be appropriate and suitable for use in the industrial development process.
The BeatBox simulation environment combines flexible script language user interface with the robust computational tools, in order to setup cardiac electrophysiology in-silico experiments without re-coding at low-level, so that cell excitation, tissue/anatomy models, stimulation protocols may be included into a BeatBox script, and simulation run either sequentially or in parallel (MPI) without re-compilation. BeatBox is a free software written in C language to be run on a Unix-based platform. It provides the whole spectrum of multi scale tissue modelling from 0-dimensional individual cell simulation, 1-dimensional fibre, 2-dimensional sheet and 3-dimensional slab of tissue, up to anatomically realistic whole heart simulations, with run time measurements including cardiac re-entry tip/filament tracing, ECG, local/global samples of any variables, etc. BeatBox solvers, cell, and tissue/anatomy models repositories are extended via robust and flexible interfaces, thus providing an open framework for new developments in the field. In this paper we give an overview of the BeatBox current state, together with a description of the main computational methods and MPI parallelisation approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.