论文标题
基于任务的，GPU加速和可靠的库，用于解决密集的非对称特征值问题
Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems
论文作者
论文摘要
在本文中，我们介绍了用于解决密集的非对称标准和广义特征值问题的Starneig库。该库是在Starpu运行时系统顶部构建的，并针对共享和分布式存储器。图书馆的某些组成部分支持GPU加速。该图书馆目前处于早期beta状态，仅支持真正的矩阵。计划对复杂矩阵进行支持以将来发布。本文针对图书馆的潜在用户。我们描述了库的设计选择和功能，并将它们与现有软件（例如Scalapack）进行了对比。 Starneig实现了Scalapack兼容性层，该层应帮助新用户过渡到Starneig。我们通过计算实验样本来证明库的性能。
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.
