Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Proceedings of the 44th annual Design Automation Conference
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Solving Sparse Linear Systems on NVIDIA Tesla GPUs
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Implementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs
SAMOS '09 Proceedings of the 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
A Parallel Preconditioned Conjugate Gradient Solver for the Poisson Problem on a Multi-GPU Platform
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Automatically tuning sparse matrix-vector multiplication for GPU architectures
HiPEAC'10 Proceedings of the 5th international conference on High Performance Embedded Architectures and Compilers
Parallel power grid analysis using preconditioned GMRES solver on CPU-GPU platforms
Proceedings of the International Conference on Computer-Aided Design
GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography
Journal of Parallel and Distributed Computing
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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.