This work is an overview of our preliminary experience in developing a high-performance iterative linear solver accelerated by GPU coprocessors. Our goal is to illustrate the advantages and difficulties encountered when deploying GPU technology to perform sparse linear algebra computations. Techniques for speeding up sparse matrix-vector product (SpMV) kernels and finding suitable preconditioning methods are discussed. Our experiments with an NVIDIA TESLA M2070 show that for unstructured matrices SpMV kernels can be up to 8 times faster on the GPU than the Intel MKL on the host Intel Xeon X5675 Processor. Overall performance of the GPU-accelerated Incomplete Cholesky (IC) factorization preconditioned CG method can outperform its CPU counterpart by a smaller factor, up to 3, and GPU-accelerated The incomplete LU (ILU) factorization preconditioned GMRES method can achieve a speed-up nearing 4. However, with better suited preconditioning techniques for GPUs, this performance can be further improved.
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Acknowledgements This work is supported by DOE under grant DE-FG 08ER 25841 and by the Minnesota Supercomputer Institute.
- GPU computing
- Preconditioned iterative methods
- Sparse matrix computations