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Conjugate-Gradients Algorithms on a Cray-T3D

机译:Cray-T3D上的共轭梯度算法

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Conjugate-Gradients algorithms are recognized as competitive, among the available fast iterative schemes, for the solution of large-scale linear systems. The paper presents a parallel implementation, on distributed memory architectures using the SPMD programming paradigm, of the CGS and BiCGSTAB methods associated with the most popular algebraic preconditioners. We analyze the programming environment supplied by a Cray-T3D to handle data communication and data distribution and compare the performance of two versions of our code: one based on the PVM message passing interface and the other one based on the Shared Memory Access Library. The influence of a block-cyclic partitioning on the performance of the algorithms is also investigated, focusing the attention on the incomplete LU factorization preconditioner. In particular we address the tradeoff between minimizing interprocessor communication and exploiting the available parallelism by a suitable data distribution. The numerical experiments, carred out with different matrix sizes, show that the block-cyclic distribution gives satisfactory results if the problem is large enough.
机译:在可用的快速迭代方案中,共轭梯度算法在解决大型线性系统中具有竞争优势。本文介绍了使用SPMD编程范例在分布式内存体系结构上与最流行的代数前置条件相关联的CGS和BiCGSTAB方法的并行实现。我们分析了Cray-T3D提供的用于处理数据通信和数据分发的编程环境,并比较了两种代码版本的性能:一种基于PVM消息传递接口,另一种基于共享内存访问库。还研究了块循环分区对算法性能的影响,将注意力集中在不完全LU分解预处理器上。特别是,我们解决了在最小化处理器间通信与通过合适的数据分发利用可用的并行性之间的权衡问题。在不同的矩阵尺寸下进行的数值实验表明,如果问题足够大,则块循环分布将给出令人满意的结果。

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