Benchmarking CPUs and GPUs on embedded platforms for software receiver usage

Smartphones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring supercomputing technology into your pocket (or into our embedded devices). This can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light onto recent embedded processor developments, benchmarks Fast Fourier Transform (FFT) and correlation algorithms on representative embedded platforms and relates the results to the use in GNSS software radios. The use of embedded CPUs for signal tracking seems to be straight forward, but more research is required to fully achieve the nominal peak performance of an embedded GPU for FFT computation. Also the electrical power consumption is measured in certain load levels.Peer ReviewedPostprint (published version

Implicit particle methods and their connection with variational data assimilation

The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. We present a new and more general derivation of this approach and extend the method to particle smoothing as well as to data assimilation for perfect models. We show that the minimizations required by implicit particle methods are similar to the ones one encounters in variational data assimilation and explore the connection of implicit particle methods with variational data assimilation. In particular, we argue that existing variational codes can be converted into implicit particle methods at a low cost, often yielding better estimates, that are also equipped with quantitative measures of the uncertainty. A detailed example is presented

Efficient prediction of broadband trailing edge noise and application to porous edge treatment

Trailing edge noise generated by turbulent flow traveling past an edge of an airfoil is one of the most essential aeroacoustic sound generation mechanisms. It is of great interest for noise problems in various areas of industrial application. First principle based CAA with short response time are needed in the industrial design process for reliable prediction of spectral differences in turbulent-boundary-layer trailing-edge noise due to design modifications. In this paper, an aeroacoustic method is studied, resting on a hybrid CFD/CAA procedure. In a first step RANS simulation provides a time-averaged solution, including the mean-flow and turbulence statistics such as length-scale, time-scale and turbulence kinetic energy. Based on these, fluctuating sound sources are then stochastically generated by the Fast Random Particle-Mesh Method to simulate in a second CAA step broadband aeroacoustic sound. From experimental findings it is well known that porous trailing edges significantly lower trailing edge noise level over a large range of frequencies reaching up to 8dB reduction. Furthermore, sound reduction depends on the porous material parameters, e.g. geometry, porosity, permeability and pore size. The paper presents first results for an extended hybrid CFD/CAA method including porous materials with prescribed parameters. To incorporate the effect of porosity, an extended formulation of the Acoustic Perturbation Equations with source terms is derived based on a reformulation of the volume averaged Navier-Stokes equations into perturbation form. Proper implementation of the Darcy and Forchheimer terms is verified for sound propagation in homogeneous and anisotropic porous medium. Sound generation is studied for a generic symmetric NACA0012 airfoil without lift to separate secondary effects of lift and camber on sound from those of the basic edge noise treatments.Comment: 37 page

Towards an adaptive hardware parallel particle filter

A particle filter is a Montecarlo-based method suitable for predicting future states of non-linear systems with non-Gaussian noise. It is based on a set of samples of the state where each individual sample is called particle. These particles are weighted according to the real measure of the state in order to estimate the future state of the system

A domain-specific language and matrix-free stencil code for investigating electronic properties of Dirac and topological materials

We introduce PVSC-DTM (Parallel Vectorized Stencil Code for Dirac and Topological Materials), a library and code generator based on a domain-specific language tailored to implement the specific stencil-like algorithms that can describe Dirac and topological materials such as graphene and topological insulators in a matrix-free way. The generated hybrid-parallel (MPI+OpenMP) code is fully vectorized using Single Instruction Multiple Data (SIMD) extensions. It is significantly faster than matrix-based approaches on the node level and performs in accordance with the roofline model. We demonstrate the chip-level performance and distributed-memory scalability of basic building blocks such as sparse matrix-(multiple-) vector multiplication on modern multicore CPUs. As an application example, we use the PVSC-DTM scheme to (i) explore the scattering of a Dirac wave on an array of gate-defined quantum dots, to (ii) calculate a bunch of interior eigenvalues for strong topological insulators, and to (iii) discuss the photoemission spectra of a disordered Weyl semimetal.Comment: 16 pages, 2 tables, 11 figure