ICS '89 Proceedings of the 3rd international conference on Supercomputing
Fast matrix multiplies using graphics hardware
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
The potential of the cell processor for scientific computing
Proceedings of the 3rd conference on Computing frontiers
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Concurrent number cruncher: a GPU implementation of a general sparse linear solver
International Journal of Parallel, Emergent and Distributed Systems
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Image reconstruction, a very compute-intense process in general, can often be reduced to large linear equation systems represented as sparse under-determined matrices. Solvers for these equation systems (not restricted to image reconstruction) spend most of their time in sparse matrix-vector multiplications (SpMV). In this paper we will present a GPU-accelerated scheme for a Conjugate Gradient (CG) solver, with focus on the SpMV. We will discuss and quantify the optimizations employed to achieve a soft-real time constraint as well as alternative solutions relying on FPGAs, the Cell Broadband Engine, a highly optimized SSE-based software implementation, and other GPU SpMV implementations.