Parallel Sparse Cholesky Factorization on a Heterogeneous Platform

スポンサーリンク

概要

• 論文の詳細を見る

• We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.

著者

• Dou Yong

  National Laboratory For Parallel And Distribution Processing National University Of Defense Technology

• Zou Dan

  National Laboratory for Parallel and Distributed Processing, National University of Defense Technology

• LI Rongchun

  National Laboratory for Parallel and Distribution Processing, National University of Defense Technology

関連論文

• FPGA-Specific Custom VLIW Architecture for Arbitrary Precision Floating-Point Arithmetic
• High performance sparse matrix-vector multiplication on FPGA
• Parallel Sparse Cholesky Factorization on a Heterogeneous Platform
• Window Memory Layout Scheme for Alternate Row-Wise/Column-Wise Matrix Access

スポンサーリンク