arXiv - CS - Mathematical Software最新文献_第2页

Flexible Multi-Dimensional FFTs for Plane Wave Density Functional Theory Codes 平面波密度函数论代码的灵活多维 FFT

arXiv - CS - Mathematical Software Pub Date : 2024-06-08 DOI: arxiv-2406.05577

Doru Thom Popovici, Mauro del Ben, Osni Marques, Andrew Canning

{"title":"Flexible Multi-Dimensional FFTs for Plane Wave Density Functional Theory Codes","authors":"Doru Thom Popovici, Mauro del Ben, Osni Marques, Andrew Canning","doi":"arxiv-2406.05577","DOIUrl":"https://doi.org/arxiv-2406.05577","url":null,"abstract":"Multi-dimensional Fourier transforms are key mathematical building blocks\u0000that appear in a wide range of applications from materials science, physics,\u0000chemistry and even machine learning. Over the past years, a multitude of\u0000software packages targeting distributed multi-dimensional Fourier transforms\u0000have been developed. Most variants attempt to offer efficient implementations\u0000for single transforms applied on data mapped onto rectangular grids. However,\u0000not all scientific applications conform to this pattern, i.e. plane wave\u0000Density Functional Theory codes require multi-dimensional Fourier transforms\u0000applied on data represented as batches of spheres. Typically, the\u0000implementations for this use case are hand-coded and tailored for the\u0000requirements of each application. In this work, we present the Fastest Fourier\u0000Transform from Berkeley (FFTB) a distributed framework that offers flexible\u0000implementations for both regular/non-regular data grids and batched/non-batched\u0000transforms. We provide a flexible implementations with a user-friendly API that\u0000captures most of the use cases. Furthermore, we provide implementations for\u0000both CPU and GPU platforms, showing that our approach offers improved execution\u0000time and scalability on the HP Cray EX supercomputer. In addition, we outline\u0000the need for flexible implementations for different use cases of the software\u0000package.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

svds-C: A Multi-Thread C Code for Computing Truncated Singular Value Decomposition svds-C：计算截断奇异值分解的多线程 C 代码

arXiv - CS - Mathematical Software Pub Date : 2024-05-29 DOI: arxiv-2405.18966

Xu Feng, Wenjian Yu, Yuyang Xie

引用次数: 0

GridapTopOpt.jl: A scalable Julia toolbox for level set-based topology optimisation GridapTopOpt.jl：基于水平集拓扑优化的可扩展 Julia 工具箱

arXiv - CS - Mathematical Software Pub Date : 2024-05-17 DOI: arxiv-2405.10478

Zachary J. Wegert, Jordi Manyer, Connor Mallon, Santiago Badia, Vivien J. Challis

{"title":"GridapTopOpt.jl: A scalable Julia toolbox for level set-based topology optimisation","authors":"Zachary J. Wegert, Jordi Manyer, Connor Mallon, Santiago Badia, Vivien J. Challis","doi":"arxiv-2405.10478","DOIUrl":"https://doi.org/arxiv-2405.10478","url":null,"abstract":"In this paper we present GridapTopOpt, an extendable framework for level\u0000set-based topology optimisation that can be readily distributed across a\u0000personal computer or high-performance computing cluster. The package is written\u0000in Julia and uses the Gridap package ecosystem for parallel finite element\u0000assembly from arbitrary weak formulations of partial differential equation\u0000(PDEs) along with the scalable solvers from the Portable and Extendable Toolkit\u0000for Scientific Computing (PETSc). The resulting user interface is intuitive and\u0000easy-to-use, allowing for the implementation of a wide range of topology\u0000optimisation problems with a syntax that is near one-to-one with the\u0000mathematical notation. Furthermore, we implement automatic differentiation to\u0000help mitigate the bottleneck associated with the analytic derivation of\u0000sensitivities for complex problems. GridapTopOpt is capable of solving a range\u0000of benchmark and research topology optimisation problems with large numbers of\u0000degrees of freedom. This educational article demonstrates the usability and\u0000versatility of the package by describing the formulation and step-by-step\u0000implementation of several distinct topology optimisation problems. The driver\u0000scripts for these problems are provided and the package source code is\u0000available at https://github$.$com/zjwegert/GridapTopOpt.jl.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PyOptInterface: Design and implementation of an efficient modeling language for mathematical optimization PyOptInterface：设计和实现高效的数学优化建模语言

arXiv - CS - Mathematical Software Pub Date : 2024-05-16 DOI: arxiv-2405.10130

Yue Yang, Chenhui Lin, Luo Xu, Wenchuan Wu

引用次数: 0

Local Adjoints for Simultaneous Preaccumulations with Shared Inputs 共享输入同时预积累的局部相邻关系

arXiv - CS - Mathematical Software Pub Date : 2024-05-13 DOI: arxiv-2405.07819

Johannes Blühdorn, Nicolas R. Gauger

引用次数: 0

Hybrid Parallel Discrete Adjoints in SU2 SU2 中的混合并行离散邻接

arXiv - CS - Mathematical Software Pub Date : 2024-05-09 DOI: arxiv-2405.06056

Johannes Blühdorn, Pedro Gomes, Max Aehle, Nicolas R. Gauger

引用次数: 0

A Sparse Tensor Generator with Efficient Feature Extraction 具有高效特征提取功能的稀疏张量生成器

arXiv - CS - Mathematical Software Pub Date : 2024-05-08 DOI: arxiv-2405.04944

Tugba Torun, Eren Yenigul, Ameer Taweel, Didem Unat

{"title":"A Sparse Tensor Generator with Efficient Feature Extraction","authors":"Tugba Torun, Eren Yenigul, Ameer Taweel, Didem Unat","doi":"arxiv-2405.04944","DOIUrl":"https://doi.org/arxiv-2405.04944","url":null,"abstract":"Sparse tensor operations are gaining attention in emerging applications such\u0000as social networks, deep learning, diagnosis, crime, and review analysis.\u0000However, a major obstacle for research in sparse tensor operations is the\u0000deficiency of a broad-scale sparse tensor dataset. Another challenge in sparse\u0000tensor operations is examining the sparse tensor features, which are not only\u0000important for revealing its nonzero pattern but also have a significant impact\u0000on determining the best-suited storage format, the decomposition algorithm, and\u0000the reordering methods. However, due to the large sizes of real tensors, even\u0000extracting these features becomes costly without caution. To address these gaps\u0000in the literature, we have developed a smart sparse tensor generator that\u0000mimics the substantial features of real sparse tensors. Moreover, we propose\u0000various methods for efficiently extracting an extensive set of features for\u0000sparse tensors. The effectiveness of our generator is validated through the\u0000quality of features and the performance of decomposition in the generated\u0000tensors. Both the sparse tensor feature extractor and the tensor generator are\u0000open source with all the artifacts available at\u0000https://github.com/sparcityeu/feaTen and https://github.com/sparcityeu/genTen,\u0000respectively.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of H-Matrix-Vector Multiplication with Floating Point Compression 使用浮点压缩的 H 矩阵-矢量乘法性能

arXiv - CS - Mathematical Software Pub Date : 2024-05-06 DOI: arxiv-2405.03456

Ronald Kriemann

引用次数: 0

Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools 使用有限元和自动微分工具在 Python 中最小化非线性能量

arXiv - CS - Mathematical Software Pub Date : 2024-05-03 DOI: arxiv-2407.04706

Michal Béreš, Jan Valdman

引用次数: 0

Finch: Sparse and Structured Array Programming with Control Flow 芬奇稀疏和结构化数组编程与控制流

arXiv - CS - Mathematical Software Pub Date : 2024-04-25 DOI: arxiv-2404.16730

Willow Ahrens, Teodoro Fields Collin, Radha Patel, Kyle Deeds, Changwan Hong, Saman Amarasinghe

{"title":"Finch: Sparse and Structured Array Programming with Control Flow","authors":"Willow Ahrens, Teodoro Fields Collin, Radha Patel, Kyle Deeds, Changwan Hong, Saman Amarasinghe","doi":"arxiv-2404.16730","DOIUrl":"https://doi.org/arxiv-2404.16730","url":null,"abstract":"From FORTRAN to NumPy, arrays have revolutionized how we express computation.\u0000However, arrays in these, and almost all prominent systems, can only handle\u0000dense rectilinear integer grids. Real world arrays often contain underlying\u0000structure, such as sparsity, runs of repeated values, or symmetry. Support for\u0000structured data is fragmented and incomplete. Existing frameworks limit the\u0000array structures and program control flow they support to better simplify the\u0000problem. In this work, we propose a new programming language, Finch, which supports\u0000both flexible control flow and diverse data structures. Finch facilitates a\u0000programming model which resolves the challenges of computing over structured\u0000arrays by combining control flow and data structures into a common\u0000representation where they can be co-optimized. Finch automatically specializes\u0000control flow to data so that performance engineers can focus on experimenting\u0000with many algorithms. Finch supports a familiar programming language of loops,\u0000statements, ifs, breaks, etc., over a wide variety of array structures, such as\u0000sparsity, run-length-encoding, symmetry, triangles, padding, or blocks. Finch\u0000reliably utilizes the key properties of structure, such as structural zeros,\u0000repeated values, or clustered non-zeros. We show that this leads to dramatic\u0000speedups in operations such as SpMV and SpGEMM, image processing, graph\u0000analytics, and a high-level tensor operator fusion interface.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0