Characterising Radio Telescope Software With the Workload Characterisation Framework

We present a modular framework, the Workload Characterisation Framework (WCF), that is developed to obtain, store and compare key characteristics of radio astronomy processing software in a reproducible way. As a demonstration, we discuss the experiences using the framework to characterise a LOFAR calibration and imaging pipeline.Instrumentatio

Apercal-The Apertif calibration pipeline

Apertif (APERture Tile In Focus) is one of the Square Kilometre Array (SKA) pathfinder facilities. The Apertif project is an upgrade to the 50-year-old Westerbork Synthesis Radio Telescope (WSRT) using phased-array feed technology. The new receivers create 40 individual beams on the sky, achieving an instantaneous sky coverage of 6.5 square degrees. The primary goal of the Apertif Imaging Survey is to perform a wide survey of 3500 square degrees (AWES) and a medium deep survey of 350 square degrees (AMES) of neutral atomic hydrogen (up to a redshift of 0.26), radio continuum emission and polarisation. Each survey pointing yields 4.6 TB of correlated data. The goal of Apercal is to process this data and fully automatically generate science ready data products for the astronomical community while keeping up with the survey observations. We make use of common astronomical software packages in combination with Python based routines and parallelisation. We use an object oriented module-based approach to ensure easy adaptation of the pipeline. A Jupyter notebook based framework allows user interaction and execution of individual modules as well as a full automatic processing of a complete survey observation. If nothing interrupts processing, we are able to reduce a single pointing survey observation on our five node cluster with 24 physical cores and 256 GB of memory each within 24 h keeping up with the speed of the surveys. The quality of the generated images is sufficient for scientific usage for 44% of the recorded data products with single images reaching dynamic ranges of several thousands. Future improvements will increase this percentage to over 80%. Our design allowed development of the pipeline in parallel to the commissioning of the Apertif system

A sparse Laplacian in tensor product wavelet coordinates

We construct a wavelet basis on the unit interval with respect to which both the (infinite) mass and stiffness matrix corresponding to the one-dimensional Laplacian are (truly) sparse and boundedly invertible. As a consequence, the (infinite) stiffness matrix corresponding to the Laplacian on the n-dimensional unit box with respect to the n-fold tensor product wavelet basis is also sparse and boundedly invertible. This greatly simplifies the implementation and improves the quantitative properties of an adaptive wavelet scheme to solve the multi-dimensional Poisson equation. The results extend to any second order partial differential operator with constant coefficients that defines a boundedly invertible operator

An adaptive wavelet method for solving high-dimensional elliptic PDEs

Adaptive tensor product wavelet methods are applied for solving Poisson’s equation, as well as anisotropic generalizations, in high space dimensions. It will be demonstrated that the resulting approximations converge in energy norm with the same rate as the best approximations from the span of the best N tensor product wavelets, where moreover the constant factor that we may lose is independent of the space dimension n. The cost of producing these approximations will be proportional to their length with a constant factor that may grow with n, but only linearly

A sampling problem from lithography for chip layout

Given a list with simple polygons and a sampling grid geometry, we calculate the sample (greyscale) value of each grid pixel, such that the residual error in a certain norm is sufficiently small, taking into account that the amount of computational operations should be minimal