Search results for "computational science"
showing 10 items of 124 documents
Nvidia CUDA parallel processing of large FDTD meshes in a desktop computer
2020
The Finite Difference in Time Domain numerical (FDTD) method is a well know and mature technique in computational electrodynamics. Usually FDTD is used in the analysis of electromagnetic structures, and antennas. However still there is a high computational burden, which is a limitation for use in combination with optimization algorithms. The parallelization of FDTD to calculate in GPU is possible using Matlab and CUDA tools. For instance, the simulation of a planar array, with a three dimensional FDTD mesh 790x276x588, for 6200 time steps, takes one day -elapsed time- using the CPU of an Intel Core i3 at 2.4GHz in a personal computer, 8Gb RAM. This time is reduced 120 times when the calcula…
Scalability of GPU-Processed 3D Distance Maps for Industrial Environments
2018
This paper contains a benchmark analysis of the open source library GPU-Voxels together with the Robot Operating System (ROS) in large-scale industrial robotics environment. Six sensor nodes with embedded computing generate real-time point cloud data as ROS topics. The overall data from all sensor nodes is processed by a combination of CPU and GPU on a central ROS node. Experimental results demonstrate that the system is able to handle frame rates of 10 and 20 Hz with voxel sizes of 4, 6, 8 and 12 cm without saturation of the CPU or the GPU used by the GPU-Voxels library. The results in this paper show that ROS, in combination with GPU-Voxels, can be used as a viable solution for real-time …
High quality conservative surface mesh generation for swept volumes
2012
We present a novel, efficient and flexible scheme to generate a high quality mesh that approximates the outer boundary of a swept volume. Our approach comes with two guarantees. First, the approximation is conservative, i.e., the swept volume is enclosed by the generated mesh. Second, the one-sided Hausdorff distance of the generated mesh to the swept volume is upper bounded by a user defined tolerance. Exploiting this tolerance the algorithm generates a mesh that is adapted to the local complexity of the swept volume boundary, keeping the overall output complexity remarkably low. The algorithm is two-phased: the actual sweep and the mesh generation. In the sweeping phase we introduce a gen…
Inferring causation from time series in earth system sciences
2019
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
NESSie.jl – Efficient and intuitive finite element and boundary element methods for nonlocal protein electrostatics in the Julia language
2018
Abstract The development of scientific software can be generally characterized by an initial phase of rapid prototyping and the subsequent transition to computationally efficient production code. Unfortunately, most programming languages are not well-suited for both tasks at the same time, commonly resulting in a considerable extension of the development time. The cross-platform and open-source Julia language aims at closing the gap between prototype and production code by providing a usability comparable to Python or MATLAB alongside high-performance capabilities known from C and C++ in a single programming language. In this paper, we present efficient protein electrostatics computations a…
Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies
2016
Mieth, Bettina et al.
Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters
2016
Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data par…
NMR Exchange Format: a unified and open standard for representation of NMR restraint data
2015
SCOPUS: le.j
Frames and weak frames for unbounded operators
2020
In 2012 G\u{a}vru\c{t}a introduced the notions of $K$-frame and of atomic system for a linear bounded operator $K$ in a Hilbert space $\mathcal{H}$, in order to decompose its range $\mathcal{R}(K)$ with a frame-like expansion. In this article we revisit these concepts for an unbounded and densely defined operator $A:\mathcal{D}(A)\to\mathcal{H}$ in two different ways. In one case we consider a non-Bessel sequence where the coefficient sequence depends continuously on $f\in\mathcal{D}(A)$ with respect to the norm of $\mathcal{H}$. In the other case we consider a Bessel sequence and the coefficient sequence depends continuously on $f\in\mathcal{D}(A)$ with respect to the graph norm of $A$.
Computational Science of Religion
2021
This article provides a basic overview of the most common methods of computer modelling and simulation that are currently being used to study religion. It focuses on the use (and illustrates the value) of system dynamics models, agent-based models, including game theory and multi-agent artificial intelligence models, and artificial neural networks. General use case examples are provided, and considerations for future research are discussed. We conclude by encouraging scholars interested in religion and related fields to incorporate techniques from the computational science of religion into their collaborative methodological toolkits.