Concurrent number cruncher - A GPU implementation of a general sparse linear sol

@inproceedings{131.buatois,
 abstract = { A wide class of numerical methods needs to solve a linear system, where the matrix pattern of non-zero
coefficients can be arbitrary. These problems can greatly benefit from highly multithreaded computational
power and large memory bandwidth available on GPUs, especially since dedicated general purpose APIs
such as CTM (AMD-ATI) and CUDA (NVIDIA) have appeared. CUDA even provides a BLAS implementation,
but only for dense matrices (CuBLAS). Other existing linear solvers for the GPU are also limited by
their internal matrix representation.
This paper describes how to combine recent GPU programming techniques and new GPU dedicated
APIs with high performance computing strategies (namely block compressed row storage, register blocking
and vectorization), to implement a sparse general-purpose linear solver. Our implementation of the Jacobipreconditioned
Conjugate Gradient algorithm outperforms by up to a factor of 11.5x leading-edge CPU
counterparts, making it attractive for applications which content with single precision. },
 author = { Buatois, Luc AND Caumon, Guillaume AND Levy, Bruno },
 booktitle = { Proc. 28th Gocad Meeting, Nancy },
 title = { Concurrent number cruncher - A GPU implementation of a general sparse linear sol },
 year = { 2008 }
}