首页/论文/2002.05024

arXiv 2002.050242020-02-12cs.MScs.DC

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen

备注：: 18 pages, 11 figures (18 when counting sub-figures), 1 tex-files. Invited article submitted to Concurrency and Computation: Practice and Experience. Second author's first name is "Carl Christian" and last name "Kjelgaard Mikkelsen"
发表信息：: Concurrency Computat Pract Exper. (2020) e5915
DOI：: 10.1002/cpe.5915

英文摘要：

In this paper, we present the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library have support for GPU acceleration. The library is currently in an early beta state and supports only real matrices. Support for complex matrices is planned for a future release. This paper is aimed at potential users of the library. We describe the design choices and capabilities of the library, and contrast them to existing software such as ScaLAPACK. StarNEig implements a ScaLAPACK compatibility layer which should assist new users in the transition to StarNEig. We demonstrate the performance of the library with a sample of computational experiments.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Introduction to StarNEig -- A Task-based Library for Solving Nonsymmetric Eigenvalue Problems

In this paper, we present the StarNEig library for solving dense non-symmetric (generalized) eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library support GPUs. The library is currently in an early beta state and only real arithmetic is supported. Support for complex data types is planned for a future release. This paper is aimed for potential users of the library. We describe the design choices and capabilities of the library, and contrast them to existing software such as ScaLAPACK…

2019-05-13cs.DCcs.MS

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Most, if not all the modern scientific simulation packages utilize matrix algebra operations. Among the operation of the linear algebra, one of the most important kernels is the multiplication of matrices, dense and sparse. Examples of application of such a kernel are in electronic structure calculations, machine learning, data mining, graph processing, and digital signal processing. Several optimized libraries exist that can achieve high-performance on distributed systems. Only a few of them target distributed GPU-accelerated systems. In most of the cases, these libraries are provided and opt…

2019-10-10cs.DCcs.MS

Enabling GPU Accelerated Computing in the SUNDIALS Time Integration Library

As part of the Exascale Computing Project (ECP), a recent focus of development efforts for the SUite of Nonlinear and DIfferential/ALgebraic equation Solvers (SUNDIALS) has been to enable GPU-accelerated time integration in scientific applications at extreme scales. This effort has resulted in several new GPU-enabled implementations of core SUNDIALS data structures, support for programming paradigms which are aware of the heterogeneous architectures, and the introduction of utilities to provide new points of flexibility. In this paper, we discuss our considerations, both internal and external,…

2021-12-08cs.DCcs.MS

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

The library PRAND for pseudorandom number generation for modern CPUs and GPUs is presented. It contains both single-threaded and multi-threaded realizations of a number of modern and most reliable generators recently proposed and studied in [1,2,3,4,5] and the efficient SIMD realizations proposed in [6]. One of the useful features for using PRAND in parallel simulations is the ability to initialize up to $10^{19}$ independent streams. Using massive parallelism of modern GPUs and SIMD parallelism of modern CPUs substantially improves performance of the generators.

2014-02-17cs.MSphysics.comp-ph

JAX-AMG：一个用于JAX的GPU加速可微分稀疏线性求解器库

JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX

Cornell University（康奈尔大学）；University of Notre Dame（圣母大学）

提出JAX-AMG，通过封装Nvidia AmgX求解器套件，为JAX生态提供GPU加速的代数多重网格求解器，支持自动微分、分布式多GPU执行，适用于PDE约束优化和反问题。

2026-07-30cs.MSphysics.comp-ph

PyFAI: a Python library for high performance azimuthal integration on GPU

The pyFAI package has been designed to reduce X-ray diffraction images into powder diffraction curves to be further processed by scientists. This contribution describes how to convert an image into a radial profile using the Numpy package, how the process was accelerated using Cython. The algorithm was parallelised, needing a complete re-design to benefit from massively parallel devices like graphical processing units or accelerators like the Intel Xeon Phi using the PyOpenCL library.

2014-12-19astro-ph.IMcs.DCcs.MS

更多相关论文收起更多论文

将Odeint时间步进集成到OpenFPM中以实现分布式且GPU加速的数值求解器

Integrating Odeint Time Stepping into OpenFPM for Distributed and GPU Accelerated Numerical Solvers

本研究将Boost的Odeint时间积分库集成到OpenFPM框架，通过扩展元编程系统支持其数据类型，实现分布式及GPU加速的显式时间积分，经基准测试与实例验证，取得了80%的强扩展效率及单GPU五倍加速的效果。

2025-10-02cs.DCcs.MS

JAX-FEM：一个可微分的GPU加速三维有限元求解器，用于自动逆向设计与机制数据科学

JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

本文提出开源可微有限元库JAX-FEM，基于Google JAX实现GPU加速与自动微分，支持全自动逆向设计与机器学习辅助计算力学的一体化求解。

2022-12-02cs.CEcs.MS

Bandicoot：用于 GPU 线性代数与科学计算的 C++ 库

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

本文介绍 Bandicoot C++ 库，它作为 Armadillo 的 GPU 加速对应版本，通过模板元编程实现编译期优化，使用户无需大幅修改现有代码即可利用 GPU 加速线性代数与科学计算。

2023-08-06cs.MS

Bandicoot：用于GPU线性代数的模板化C++库

Bandicoot: A Templated C++ Library for GPU Linear Algebra

NumFOCUS；Collabora；CSIRO（澳大利亚联邦科学与工业研究组织）；Griffith University（格里菲斯大学）

Bandicoot是适配Armadillo的GPU线性代数C++库，通过模板元编程实现编译时优化，可加速原有代码的GPU计算，性能优于Armadillo的CPU版本，为开源软件。

2026-08-12cs.MS

多GPU平台上微分方程的自动翻译与加速求解

Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms

该方法实现GPU上ODE和SDE集合的高效并行求解，无需用户改码，性能优于JAX和PyTorch的vmap方法，并支持多GPU及多种GPU架构。

2023-11-13cs.DCcs.MScs.NA

ChASE: Chebyshev Accelerated Subspace iteration Eigensolver for sequences of Hermitian eigenvalue problems

Solving dense Hermitian eigenproblems arranged in a sequence with direct solvers fails to take advantage of those spectral properties which are pertinent to the entire sequence, and not just to the single problem. When such features take the form of correlations between the eigenvectors of consecutive problems, as is the case in many real-world applications, the potential benefit of exploiting them can be substantial. We present ChASE, a modern algorithm and library based on subspace iteration with polynomial acceleration. Novel to ChASE is the computation of the spectral estimates that enter …

2018-05-25cs.CEcs.DScs.MS

PyTorch中的有限维I型冯·诺伊曼代数：随机块对角算子的GPU加速框架

Finite-Dimensional Type I von Neumann Algebras in PyTorch: A GPU-Accelerated Framework for Random Block-Diagonal Operators

提出一个基于PyTorch的开源库torch_vn_algebra，用于有限维I型冯·诺伊曼代数的数值实验，支持随机算子生成、函数演算和迹泛函，并通过GPU加速实现中等规模蒙特卡洛研究。

2026-10-21cs.MScs.NAmath.NA

Optimizations of the Eigensolvers in the ELPA Library

The solution of (generalized) eigenvalue problems for symmetric or Hermitian matrices is a common subtask of many numerical calculations in electronic structure theory or materials science. Solving the eigenvalue problem can easily amount to a sizeable fraction of the whole numerical calculation. For researchers in the field of computational materials science, an efficient and scalable solution of the eigenvalue problem is thus of major importance. The ELPA-library is a well-established dense direct eigenvalue solver library, which has proven to be very efficient and scalable up to very large …

2018-11-03cs.MS

Algorithm 1019: A Task-based Multi-shift QR/QZ Algorithm with Aggressive Early Deflation

The QR algorithm is one of the three phases in the process of computing the eigenvalues and the eigenvectors of a dense nonsymmetric matrix. This paper describes a task-based QR algorithm for reducing an upper Hessenberg matrix to real Schur form. The task-based algorithm also supports generalized eigenvalue problems (QZ algorithm) but this paper concentrates on the standard case. The task-based algorithm adopts previous algorithmic improvements, such as tightly-coupled multi-shifts and Aggressive Early Deflation (AED), and also incorporates several new ideas that significantly improve the per…

2021-12-15cs.DCcs.MScs.NA

$\ exttt{bucket-graph-spprc}$：一个用于带资源约束最短路径问题的可扩展C++库

$\texttt{bucket-graph-spprc}$: an extensible C++ library for the shortest path problem with resource constraints

提出一个开源C++库，实现桶图标记算法，通过编译时资源概念和SIMD加速支配，在共享实例上比PathWyse快1.3-2.35倍。

2026-07-01cs.DScs.MSmath.OC

GPU Accelerated Automatic Differentiation With Clad

Automatic Differentiation (AD) is instrumental for science and industry. It is a tool to evaluate the derivative of a function specified through a computer program. The range of AD application domain spans from Machine Learning to Robotics to High Energy Physics. Computing gradients with the help of AD is guaranteed to be more precise than the numerical alternative and have a low, constant factor more arithmetical operations compared to the original function. Moreover, AD applications to domain problems typically are computationally bound. They are often limited by the computational requiremen…

2022-05-16cs.MS

cuHPX：HEALPix网格上的GPU加速可微分球谐变换

cuHPX: GPU-Accelerated Differentiable Spherical Harmonic Transforms on HEALPix Grids

本研究针对HEALPix网格上高性能球谐变换实现难的问题，提出优化的CUDA库cuHPX，兼具高精度、可微性与内存效率，提速超现有库20倍，支撑多领域科学应用。

2025-10-02astro-ph.IMcs.MS

使用加速关联（令牌）数组分析10 Petabit/s网络数据

Analyzing 10 Petabit/s Network Data with Accelerated Associative (Token) Arrays

MIT（麻省理工学院）

针对大规模网络隐私分析需求，利用GPU加速的D4M关联数组库，在多种硬件上实现水平线性扩展，达到可分析10 Petabit/s网络的持续处理速率。

2026-09-29cs.CRcs.DCcs.MS