A High Performance and Memory Efficient LU Decomposer on FPGAs

Abstract Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

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VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm

Journal of Healthcare Engineering ◽

10.1155/2021/6297856 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Osamah Ibrahim Khalaf ◽

Carlos Andrés Tavera Romero ◽

A. Azhagu Jaisudhan Pazhani ◽

G. Vinuja

Keyword(s):

High Performance ◽

Low Cost ◽

Vlsi Architecture ◽

Linear Interpolation ◽

Noisy Environments ◽

Image Scaling ◽

Scaling Methods ◽

Processing Resources ◽

Weighted Median ◽

Memory Efficient

This study implements the VLSI architecture for nonlinear-based picture scaling that is minimal in complexity and memory efficient. Image scaling is used to increase or decrease the size of an image in order to map the resolution of different devices, particularly cameras and printers. Larger memory and greater power are also necessary to produce high-resolution photographs. As a result, the goal of this project is to create a memory-efficient low-power image scaling methodology based on the effective weighted median interpolation methodology. Prefiltering is employed in linear interpolation scaling methods to improve the visual quality of the scaled image in noisy environments. By decreasing the blurring effect, the prefilter performs smoothing and sharpening processes to produce high-quality scaled images. Despite the fact that prefiltering requires more processing resources, the suggested solution scales via effective weighted median interpolation, which reduces noise intrinsically. As a result, a low-cost VLSI architecture can be created. The results of simulations reveal that the effective weighted median interpolation outperforms other existing approaches.

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Memory-Efficient and High-Performance Two-Dimensional Discrete Wavelet Transform Architecture Based on Decomposed Lifting Algorithm

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e92.a.2000 ◽

2009 ◽

Vol E92-A (8) ◽

pp. 2000-2008

Author(s):

Peng CAO ◽

Chao WANG ◽

Longxing SHI

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

High Performance ◽

Discrete Wavelet ◽

Two Dimensional ◽

Memory Efficient

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Parallel high-performance computing algorithm to generate FEM-compliant volumetric mesh representations of biomolecules at atomic scale

10.1101/578658 ◽

2019 ◽

Author(s):

Jorge López ◽

Salvador Botello ◽

Rafael Herrera ◽

Mauricio Carrillo-Tripp

Keyword(s):

Finite Element ◽

High Performance Computing ◽

Mesh Generation ◽

High Performance ◽

Computational Study ◽

Atomic Scale ◽

Element Analysis ◽

Performance Computing ◽

Mesh Models ◽

Memory Efficient

AbstractThe computational study of biomolecules has been undermined by the lack of models that accurately represent the structure of big complexes at the atomic level. In this work, we report the development of an algorithm to generate a volumetric mesh of a biomolecule, of any size and shape, based on its atomic structure. Our mesh generation tool leverages the octree algorithm properties with parallel high-performance computing techniques to produce a discretized hexahedral model faster than previous methods. The reported algorithm is memory efficient and generates volumetric meshes suitable to be used directly in Finite Element Analysis. We tested the algorithm by producing mesh models of different biomolecule types and complex size, and also performed numerical simulations for the largest case. The Finite Element results show that our mesh models reproduce experimental data.

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