Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Lattice Boltzmann based PDE solver on the GPU
The Visual Computer: International Journal of Computer Graphics
Accelerating geoscience and engineering system simulations on graphics hardware
Computers & Geosciences
Mersenne Twister Random Number Generation on FPGA, CPU and GPU
AHS '09 Proceedings of the 2009 NASA/ESA Conference on Adaptive Hardware and Systems
IEEE Micro
State-of-the-art in heterogeneous computing
Scientific Programming
Novel Architectures: Solving Computational Problems with GPU Computing
Computing in Science and Engineering
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Graphics processing units GPUs are rapidly emerging as a more economical and highly competitive alternative to CPU-based parallel computing. As the degree of software control of GPUs has increased, many researchers have explored their use in non-gaming applications. Recent studies have shown that GPUs consistently outperform their best corresponding CPU-based parallel computing alternatives in single-instruction multiple-data SIMD strategies. This study explores the use of GPUs for uncertainty quantification in computational mechanics. Five types of analysis procedures that are frequently utilized for uncertainty quantification of mechanical and dynamical systems have been considered and their GPU implementations have been developed. The numerical examples presented in this study show that considerable gains in computational efficiency can be obtained for these procedures. It is expected that the GPU implementations presented in this study will serve as initial bases for further developments in the use of GPUs in the field of uncertainty quantification and will i aid the understanding of the performance constraints on the relevant GPU kernels and ii provide some guidance regarding the computational and the data structures to be utilized in these novel GPU implementations.