SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Solving Linear Systems on Vector and Shared Memory Computers
Solving Linear Systems on Vector and Shared Memory Computers
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
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Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Solving Systems of Linear Equations on the CELL Processor Using Cholesky Factorization
IEEE Transactions on Parallel and Distributed Systems
Parallel Simulation of Oil Reservoirs on a Multi-core Stream Computer
Transactions on Computational Science III
A comparison of parallel gaussian elimination solvers for the computation of electrochemical battery models on the cell processor
Parallelization and Performance Analysis of an IMPES-based Oil-Water Reservoir Simulator
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Perfomance Models for Blocked Sparse Matrix-Vector Multiplication Kernels
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
CG-Cell: an NPB benchmark implementation on cell broadband engine
ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
Winner: Geophysics Solving the Oil Equation
IEEE Spectrum
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This paper presents our implementation of the method of parallel conjugate gradients (CGs) on the Cell Broadband Engine®(Cell/B.E.®). The solution of linear systems of equations is one of the most central-processing-unit-intensive steps in oil reservoir simulation applications and can greatly benefit from the multitude of single-instruction-multiple-data-capable synergistic processor element (SPE) cores in the Cell/B.E. processor. We assume that the linear system of equations is of standard form Ax = B, where A is a square sparse coefficient matrix. Several solvers exist with distinct advantages and disadvantages. When dealing with 1-D, 2-D, and 3-D reservoirs, the resulting coefficient matrix can be formulated as a banded matrix. This paper reports the implementation of the serial CG on the Cell/B.E. PowerPC®processor element (PPE) and the parallelization and performance analysis of CG across 1, 8, and 16 SPEs for tridiagonal (1-D reservoir grid), pentadiagonal (2-D reservoir grid), and heptadiagonal (3-D reservoir grid) matrices. Our implementation is shown to scale well with data size, grid dimensionality, and number of cores.