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
Automatic performance tuning of sparse matrix kernels
Automatic performance tuning of sparse matrix kernels
Parallel Computing Experiences with CUDA
IEEE Micro
Solving Sparse Linear Systems on NVIDIA Tesla GPUs
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Fast Conjugate Gradients with Multiple GPUs
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Concurrent number cruncher: an efficient sparse linear solver on the GPU
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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After more than five years since GPUs were first used as accelerators for general scientific computations, the field of General Purpose GPU computing or GPGPU has finally reached mainstream. Developers have now access to a mature hardware and software ecosystem. On the software side, several major open-source packages now support GPU acceleration while on the hardware side cloud-based solutions provide a simple way to access powerful machines with the latest GPUs at low cost. In this context, we look at the GPU acceleration of CAE, with a focus on the matrix solvers. We compare the performance that can be achieved using the open-source solver package PETSc ran on GPU-enabled Amazon EC2 hardware with that of an optimized legacy FEM code ran on a last generation 12-core blade server. Our results show that, although good performance can be achieved, some development is still needed to achieve peak performance.