Title :
Triangular matrix inversion on Graphics Processing Unit
Author :
Ries, F. ; De Marco, Tommaso ; Zivieri, M. ; Guerrieri, Roberto
Author_Institution :
ARCES, Univ. of Bologna, Bologna, Italy
Abstract :
Dense matrix inversion is a basic procedure in many linear algebra algorithms. A computationally arduous step in most dense matrix inversion methods is the inversion of triangular matrices as produced by factorization methods such as LU decomposition. In this paper, we demonstrate how triangular matrix inversion (TMI) can be accelerated considerably by using commercial Graphics Processing Units (GPU) in a standard PC. Our implementation is based on a divide and conquer type recursive TMI algorithm, efficiently adapted to the GPU architecture. Our implementation obtains a speedup of 34x versus a CPU-based LAPACK reference routine, and runs at up to 54 gigaflops/s on a GTX 280 in double precision. Limitations of the algorithm are discussed, and strategies to cope with them are introduced. In addition, we show how inversion of an L- and U-matrix can be performed concurrently on a GTX 295 based dual-GPU system at up to 90 gigaflops/s.
Keywords :
graphics processing units; linear algebra; matrix decomposition; matrix inversion; CPU based LAPACK reference routine; GPU architecture; GTX 280; GTX 295; LU decomposition; commercial graphics processing units; dense matrix inversion; double precision; dual GPU system; factorization method; linear algebra algorithm; recursive TMI algorithm; triangular matrices; triangular matrix inversion; CUDA; GPGPU; dense matrix inversion; trianguar matrix;
Conference_Titel :
High Performance Computing Networking, Storage and Analysis, Proceedings of the Conference on
Conference_Location :
Portland, OR
DOI :
10.1145/1654059.1654069