1,721,036 research outputs found
Conservation Law Analysis in Numerical Schema for a Tumor Angiogenesis PDE System
No full textTumor angiogenesis, the formation of new blood vessels from pre-existing vasculature, is a crucial process in cancer growth and metastasis. Mathematical modeling through partial differential equations helps to understand this complex biological phenomenon. Here, we provide a conservation properties analysis in a tumor angiogenesis model describing the evolution of endothelial cells, proteases, inhibitors, and extracellular matrix. The adopted approach introduces a numerical framework that combines spatial and time discretization techniques. Here, we focus on maintaining solution accuracy while preserving physical quantities during the simulation process. The method achieved second-order accuracy in both space and time discretizations, with conservation errors showing consistent convergence as the mesh was refined. The numerical schema demonstrates stable wave propagation patterns, in agreement with experimental observations. Numerical experiments validate the approach and demonstrate its reliability for long-term angiogenesis simulations
Towards a Parallel Code for Cellular Behavior in Vitro Prediction
No full textIn recent years, there has been an increasing interest in developing in vitro models that predict the behavior of cells in living organisms. Mathematical models based on differential equations, and related numerical algorithms, have been provided to this aim. In this work, we present first experiences in designing parallel strategies for accelerating an algorithm for behavior prediction based on the Cellular Potts Model (CPM). In particular, we exploit the computational power of Graphic Process Units in CUDA environment to address main low-level kernels involved. Tests and experiments complete the paper
An accelerated algorithm for ECG signal denoising
No full textThe Electrocardiogram (ECG) signal is an important tool for cardiovascular diseases analysis. However, still today acquisition devices produce noisy signals that degrades the quality of information by corrupting important features. To improve the quality of the acquired data a filtering process is mandatory. Moreover, a real-time filtering of ECGs, in order to obtain a diagnosis as quickly as possible is a very interesting challenge. In this paper, we consider as denoising filter, the Savitzky-Golay method and we propose a parallel algorithm implementing it. The procedure exploits the computational power of Graphics Processing Units (GPUs). Results in terms of performance and quality are provided
Energy performance profiling of a GPU-based CPM implementation
No full textIn recent years, there has been a growing interest in the development of in vitro models to predict cellular behavior within living organisms. Mathematical models, based on differential equations and associated numerical algorithms, have been employed for this purpose. In this study, we present initial forays into the design of parallel strategies aimed at accelerating an algorithm for behavior prediction, specifically based on the Cellular Potts Model. To do this, we engage the computational power of Graphic Processing Units within the CUDA environment to optimize critical low-level kernels.This work intends to provide a comprehensive analysis of the energy performance of the proposed implementation. Tests and experiments affirm significant performance gains in terms of both processing time and substantial energy savings
Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
No full textPower consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations of a Cellular Potts model, which models cellular behavior through Hamiltonian energy minimization techniques, leveraging modern GPU architectures. By evaluating alternative solvers, it demonstrates that specific methods can significantly enhance computational efficiency and reduce energy use compared to traditional approaches. The results confirm notable improvements in execution time and energy consumption. In particular, the experiments show a reduction in terms of power of up to 53%, providing a pathway towards more sustainable high-performance computing practices for complex biological simulations
Parallel solvers comparison for an inverse problem in fractional calculus
No full textHigh-Performance Computing (HPC) is a fundamental tool for improving the performance of many algorithms in terms of time, especially for large-scale problems. In the last years, various HPC architectures have been developed to quickly process data in many research areas and at the same time the HPC tools has become very important. In addition, the development of scientific libraries for parallel computing plays a key role in achieving better performance. In particular, thanks to the computational power of Graphic Processing Units, themost popular and inexpensive accelerators, the parallel computing field has become almost a standard process for datamanagement. Hence, the porting of many standard numerical libraries on these architectures produced excellent results. In this work, we deal with a two-dimensional time fractional diffusion problem. More in detail, we analyze the performance of some parallel codes, specifically designed to solve it, implemented in different architectures. Moreover, a further GPU version is proposed and compared with the above implementations
A GPU-Based Algorithm for Environmental Data Filtering
No full textNowadays, the Machine Learning (ML) approach is needful to many research fields. Among these, the Environmental Science (ES) which involves a large amount of data to be processed and collected. On the other hand, in order to provide a reliable output, those data information must be assimilated. Since this process requires a large execution time when the input dataset is very huge, here we propose a parallel GPU algorithm based on a curve fitting method, to filter the starting dataset, by exploiting the computational power of the CUDA tool. The innovative aspect of the proposed procedure can be used in several application fields. Our experiments show the achieved results in terms of performance
A Novel GPU Implementation for Image Stripe Noise Removal
No full textImage processing is a class of procedures very helpful in several research fields. In a general scheme, a starting image generates a output image, or some image features, whose values are composed by using different methods. In particular, among image processing procedures, image restoration represents a current challenge to address. In this context the noise removal plays a central role. Here, we consider the specific problem of stripe noise removal. To this aim, in this paper we propose a novel Gaussian-based method that works in the frequency domain. Due to the large computational cost when using, in general, Gaussian related methods, a suitable parallel algorithm is presented. The parallel implementation is based on a specific strategy which relies the newest powerful of graphic accelerator such as NVIDIA GPUs, by combining CUDA kernels and OpenACC’s routines. The proposed algorithm exhibits good performance in term of quality and execution times. Tests and experiments show the quality of the restored images and the achieved performance
Numerical Solution of Diffusion Models in Biomedical Imaging on Multicore Processors
Full text linkIn this paper, we consider nonlinear partial differential equations (PDEs) of diffusion/advection type underlying most problems in image analysis. As case study, we address the segmentation of medical structures. We perform a comparative study of numerical algorithms arising from using the semi-implicit and the fully implicit discretization schemes. Comparison criteria take into account both the accuracy and the efficiency of the algorithms. As measure of accuracy, we consider the Hausdorff distance and the residuals of numerical solvers, while as measure of efficiency we consider convergence history, execution time, speedup, and parallel efficiency. This analysis is carried out in a multicore-based parallel computing environment
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