ACM Transactions on Mathematical Software (TOMS)最新文献_第9页

Replicated Computational Results (RCR) Report for “Code Generation for Generally Mapped Finite Elements” “一般映射有限元代码生成”的重复计算结果(RCR)报告

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-12-01 DOI: 10.1145/3360984

Neil Lindquist

引用次数: 0

Automating the Formulation and Resolution of Convex Variational Problems 凸变分问题的自动表述与求解

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-11-29 DOI: 10.1145/3393881

J. Bleyer

引用次数: 10

The Linear Algebra Mapping Problem. Current State of Linear Algebra Languages and Libraries 线性代数映射问题。线性代数语言和库的现状

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-11-21 DOI: 10.1145/3549935

C. Psarras, Henrik Barthels, P. Bientinesi

{"title":"The Linear Algebra Mapping Problem. Current State of Linear Algebra Languages and Libraries","authors":"C. Psarras, Henrik Barthels, P. Bientinesi","doi":"10.1145/3549935","DOIUrl":"https://doi.org/10.1145/3549935","url":null,"abstract":"We observe a disconnect between developers and end-users of linear algebra libraries. On the one hand, developers invest significant effort in creating sophisticated numerical kernels. On the other hand, end-users are progressively less likely to go through the time consuming process of directly using said kernels; instead, languages and libraries, which offer a higher level of abstraction, are becoming increasingly popular. These languages offer mechanisms that internally map the input program to lower level kernels. Unfortunately, our experience suggests that, in terms of performance, this translation is typically suboptimal. In this paper, we define the problem of mapping a linear algebra expression to a set of available building blocks as the “Linear Algebra Mapping Problem” (LAMP); we discuss its NP-complete nature, and investigate how effectively a benchmark of test problems is solved by popular high-level programming languages and libraries. Specifically, we consider Matlab, Octave, Julia, R, Armadillo (C++), Eigen (C++), and NumPy (Python); the benchmark is meant to test both compiler optimizations, as well as linear algebra specific optimizations, such as the optimal parenthesization of matrix products. The aim of this study is to facilitate the development of languages and libraries that support linear algebra computations.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"1 1","pages":"1 - 30"},"PeriodicalIF":0.0,"publicationDate":"2019-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81440363","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: 7

Abstractions and Automated Algorithms for Mixed Domain Finite Element Methods 混合域有限元方法的抽象与自动算法

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-11-04 DOI: 10.1145/3471138

Cécile Daversin-Catty, C. Richardson, A. J. Ellingsrud, M. Rognes

引用次数: 7

Propagating Geometry Information to Finite Element Computations 将几何信息传播到有限元计算

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-10-22 DOI: 10.1145/3468428

L. Heltai, W. Bangerth, M. Kronbichler, A. Mola

{"title":"Propagating Geometry Information to Finite Element Computations","authors":"L. Heltai, W. Bangerth, M. Kronbichler, A. Mola","doi":"10.1145/3468428","DOIUrl":"https://doi.org/10.1145/3468428","url":null,"abstract":"The traditional workflow in continuum mechanics simulations is that a geometry description —for example obtained using Constructive Solid Geometry (CSG) or Computer Aided Design (CAD) tools—forms the input for a mesh generator. The mesh is then used as the sole input for the finite element, finite volume, and finite difference solver, which at this point no longer has access to the original, “underlying” geometry. However, many modern techniques—for example, adaptive mesh refinement and the use of higher order geometry approximation methods—really do need information about the underlying geometry to realize their full potential. We have undertaken an exhaustive study of where typical finite element codes use geometry information, with the goal of determining what information geometry tools would have to provide. Our study shows that nearly all geometry-related needs inside the simulators can be satisfied by just two “primitives”: elementary queries posed by the simulation software to the geometry description. We then show that it is possible to provide these primitives in all of the frequently used ways in which geometries are described in common industrial workflows, and illustrate our solutions using a number of examples.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"60 1","pages":"1 - 30"},"PeriodicalIF":0.0,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83729529","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: 10

PySPH: A Python-based Framework for Smoothed Particle Hydrodynamics PySPH:基于python的光滑粒子流体力学框架

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-09-10 DOI: 10.1145/3460773

P. Ramachandran, K. Puri, Aditya Bhosale, A. Dinesh, Abhinav Muta, P. Negi, R. Govind, Suraj Sanka, Pankaj Pandey, Chandrashekhar Kaushik, Anshuman Kumar, Ananyo Sen, Rohan Kaushik, Mrinalgouda Patil, Deep Tavker, Dileep P. Menon, V. Kurapati, Amal Sebastian, A. Dutt, A. Agarwal

{"title":"PySPH: A Python-based Framework for Smoothed Particle Hydrodynamics","authors":"P. Ramachandran, K. Puri, Aditya Bhosale, A. Dinesh, Abhinav Muta, P. Negi, R. Govind, Suraj Sanka, Pankaj Pandey, Chandrashekhar Kaushik, Anshuman Kumar, Ananyo Sen, Rohan Kaushik, Mrinalgouda Patil, Deep Tavker, Dileep P. Menon, V. Kurapati, Amal Sebastian, A. Dutt, A. Agarwal","doi":"10.1145/3460773","DOIUrl":"https://doi.org/10.1145/3460773","url":null,"abstract":"PySPH is an open-source, Python-based, framework for particle methods in general and Smoothed Particle Hydrodynamics (SPH) in particular. PySPH allows a user to define a complete SPH simulation using pure Python. High-performance code is generated from this high-level Python code and executed on either multiple cores, or on GPUs, seamlessly. It also supports distributed execution using MPI. PySPH supports a wide variety of SPH schemes and formulations. These include, incompressible and compressible fluid flow, elastic dynamics, rigid body dynamics, shallow water equations, and other problems. PySPH supports a variety of boundary conditions including mirror, periodic, solid wall, and inlet/outlet boundary conditions. The package is written to facilitate reuse and reproducibility. This article discusses the overall design of PySPH and demonstrates many of its features. Several example results are shown to demonstrate the range of features that PySPH provides.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"55 1","pages":"1 - 38"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80438136","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: 33

hIPPYlib

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-09-09 DOI: 10.1145/3428447

U. Villa, N. Petra, O. Ghattas

{"title":"hIPPYlib","authors":"U. Villa, N. Petra, O. Ghattas","doi":"10.1145/3428447","DOIUrl":"https://doi.org/10.1145/3428447","url":null,"abstract":"We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"2 1","pages":"1 - 34"},"PeriodicalIF":0.0,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90911479","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: 67

ArborX

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-08-16 DOI: 10.1145/3412558

D. Lebrun-Grandié, A. Prokopenko, Bruno Turcksin, S. Slattery

引用次数: 9

A Shift Selection Strategy for Parallel Shift-invert Spectrum Slicing in Symmetric Self-consistent Eigenvalue Computation 对称自洽特征值计算中并行移反频谱切片的移位选择策略

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-08-16 DOI: 10.1145/3409571

David B. Williams-Young, Paul G. Beckman, Chao Yang

{"title":"A Shift Selection Strategy for Parallel Shift-invert Spectrum Slicing in Symmetric Self-consistent Eigenvalue Computation","authors":"David B. Williams-Young, Paul G. Beckman, Chao Yang","doi":"10.1145/3409571","DOIUrl":"https://doi.org/10.1145/3409571","url":null,"abstract":"The central importance of large-scale eigenvalue problems in scientific computation necessitates the development of massively parallel algorithms for their solution. Recent advances in dense numerical linear algebra have enabled the routine treatment of eigenvalue problems with dimensions on the order of hundreds of thousands on the world’s largest supercomputers. In cases where dense treatments are not feasible, Krylov subspace methods offer an attractive alternative due to the fact that they do not require storage of the problem matrices. However, demonstration of scalability of either of these classes of eigenvalue algorithms on computing architectures capable of expressing massive parallelism is non-trivial due to communication requirements and serial bottlenecks, respectively. In this work, we introduce the SISLICE method: a parallel shift-invert algorithm for the solution of the symmetric self-consistent field (SCF) eigenvalue problem. The SISLICE method drastically reduces the communication requirement of current parallel shift-invert eigenvalue algorithms through various shift selection and migration techniques based on density of states estimation and k-means clustering, respectively. This work demonstrates the robustness and parallel performance of the SISLICE method on a representative set of SCF eigenvalue problems and outlines research directions that will be explored in future work.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"40 1","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90159046","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: 5

SODECL

ACM Transactions on Mathematical Software (TOMS) Pub Date : 2019-08-11 DOI: 10.1145/3385076

Eleftherios Avramidis, Marta Lalik, O. Akman

{"title":"SODECL","authors":"Eleftherios Avramidis, Marta Lalik, O. Akman","doi":"10.1145/3385076","DOIUrl":"https://doi.org/10.1145/3385076","url":null,"abstract":"Stochastic differential equations (SDEs) are widely used to model systems affected by random processes. In general, the analysis of an SDE model requires numerical solutions to be generated many times over multiple parameter combinations. However, this process often requires considerable computational resources to be practicable. Due to the embarrassingly parallel nature of the task, devices such as multi-core processors and graphics processing units (GPUs) can be employed for acceleration. Here, we present SODECL (https://github.com/avramidis/sodecl), a software library that utilizes such devices to calculate multiple orbits of an SDE model. To evaluate the acceleration provided by SODECL, we compared the time required to calculate multiple orbits of an exemplar stochastic model when one CPU core is used, to the time required when using all CPU cores or a GPU. In addition, to assess scalability, we investigated how model size affected execution time on different parallel compute devices. Our results show that when using all 32 CPU cores of a high-end high-performance computing node, the task is accelerated by a factor of up to ≈6.7, compared to when using a single CPU core. Executing the task on a high-end GPU yielded accelerations of up to ≈4.5, compared to a single CPU core.","PeriodicalId":7036,"journal":{"name":"ACM Transactions on Mathematical Software (TOMS)","volume":"11 1","pages":"1 - 21"},"PeriodicalIF":0.0,"publicationDate":"2019-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87315112","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: 2