基于多线程结构的稠密广义特征值问题求解
Solving Dense Generalized Eigenproblems on Multi-threaded Architectures
摘要：
我们比较了两种方法来计算一部分的频谱密集对称定广义特征值问题：一个是基于减少到三对角形式，和其他Krylov子空间迭代。两个大规模的应用，产生在分子动力学和材料科学，调查的应用程序，架构和并行的方法的性能的求解器的贡献。在配备图形处理单元（GPU）的最先进的8核平台上的实验结果表明，在实际应用中，迭代Krylov子空间方法也可以是解决密集问题的有竞争力的方法。
英文摘要：
We compare two approaches to compute a portion of the spectrum of dense symmetric definite generalized eigenproblems: one is based on the reduction to tridiagonal form, and the other on the Krylov-subspace iteration. Two large-scale applications, arising in molecular dynamics and material science, are employed to investigate the contributions of the application, architecture, and parallelism of the method to the performance of the solvers. The experimental results on a state-of-the-art 8-core platform, equipped with a graphics processing unit (GPU), reveal that in real applications, iterative Krylov-subspace methods can be a competitive approach also for the solution of dense problems.
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