scholarly journals Software Prefetching for Unstructured Mesh Applications

Author(s):  
Ioan Hadade ◽  
Timothy M. Jones ◽  
Feng Wang ◽  
Luca di Mare
2020 ◽  
Vol 7 (1) ◽  
pp. 1-23
Author(s):  
Ioan Hadade ◽  
Timothy M. Jones ◽  
Feng Wang ◽  
Luca di Mare

Kerntechnik ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. 262-266
Author(s):  
M. Lovecký ◽  
J. Závorka ◽  
J. Vimpel

2019 ◽  
Author(s):  
Joshua Bradly Spencer ◽  
Jennifer Louise Alwin
Keyword(s):  

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 444 ◽  
Author(s):  
Jinxi Li ◽  
Jie Zheng ◽  
Jiang Zhu ◽  
Fangxin Fang ◽  
Christopher. Pain ◽  
...  

Advection errors are common in basic terrain-following (TF) coordinates. Numerous methods, including the hybrid TF coordinate and smoothing vertical layers, have been proposed to reduce the advection errors. Advection errors are affected by the directions of velocity fields and the complexity of the terrain. In this study, an unstructured adaptive mesh together with the discontinuous Galerkin finite element method is employed to reduce advection errors over steep terrains. To test the capability of adaptive meshes, five two-dimensional (2D) idealized tests are conducted. Then, the results of adaptive meshes are compared with those of cut-cell and TF meshes. The results show that using adaptive meshes reduces the advection errors by one to two orders of magnitude compared to the cut-cell and TF meshes regardless of variations in velocity directions or terrain complexity. Furthermore, adaptive meshes can reduce the advection errors when the tracer moves tangentially along the terrain surface and allows the terrain to be represented without incurring in severe dispersion. Finally, the computational cost is analyzed. To achieve a given tagging criterion level, the adaptive mesh requires fewer nodes, smaller minimum mesh sizes, less runtime and lower proportion between the node numbers used for resolving the tracer and each wavelength than cut-cell and TF meshes, thus reducing the computational costs.


Author(s):  
Marina Shimchenko ◽  
Rubén Titos-Gil ◽  
Ricardo Fernández-Pascual ◽  
Manuel E. Acacio ◽  
Stefanos Kaxiras ◽  
...  

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jens Zentgraf ◽  
Sven Rahmann

Abstract Motivation With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species’ (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. Results We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further. Availability Our software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort. It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.


2019 ◽  
Author(s):  
Ningli Chen ◽  
Yaping Hu ◽  
Honghu Ji ◽  
Yongqing Yuan ◽  
Guangzhou Cao

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