• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs
【24h】

Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs

机译：NVIDIA GPU的批量Cholesky分解和解决方案的实现和优化

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Many problems in engineering and scientific computing require the solution of a large number of small systems of linear equations. Due to their high processing power, Graphics Processing Units became an attractive target for this class of problems, and routines based on the LU and the QR factorization have been provided by NVIDIA in the cuBLAS library. This work addresses the situation where the systems of equations are symmetric positive definite. The paper describes the implementation and tuning of the kernels for the Cholesky factorization and the forward and backward substitution. Targeted workloads involve the solution of thousands of linear systems of the same size, where the focus is on matrix dimensions from 5 by 5 to 100 by 100. Due to the lack of a cuBLAS Cholesky factorization, execution rates of cuBLAS LU and cuBLAS QR are used for comparison against the proposed Cholesky factorization in this work. Execution rates of forward and backward substitution routines are compared to equivalent cuBLAS routines. Comparisons against optimized multicore implementations are also presented. Superior performance is reached in all cases.
机译：工程和科学计算中的许多问题都需要解决大量小型线性方程组的问题。由于其强大的处理能力，图形处理单元已成为此类问题的诱人目标，NVIDIA在cuBLAS库中提供了基于LU和QR分解的例程。这项工作解决了方程组是对称正定的情况。本文介绍了用于Cholesky分解以及正向和反向替换的内核的实现和调整。目标工作负载涉及数千个相同大小的线性系统的解决方案，其中重点放在从5 x 5到100 x 100的矩阵尺寸上。由于缺少cuBLAS Cholesky分解，因此cuBLAS LU和cuBLAS QR的执行率很高用于与这项工作中建议的Cholesky因式分解进行比较。将向前和向后替换例程的执行率与等效的cuBLAS例程进行比较。还介绍了与优化的多核实现的比较。在所有情况下都可以达到卓越的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号