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<p><it>Abstract</it>—In order to execute a parallel PDE (partial differential equation) solver on a shared-memory multiprocessor, we have to avoid memory conflicts in accessing multidimensional data grids. A new multicoloring technique is proposed for speeding sparse matrix operations. The new technique enables parallel access of grid-structured data elements in the shared memory without causing conflicts. The coloring scheme is formulated as an algebraic mapping which can be easily implemented with low overhead on commercial multiprocessors. The proposed multicoloring scheme has been tested on an Alliant FX/80 multiprocessor for solving 2D and 3D problems using the CGNR method. Compared to the results reported by Saad (1989) on an identical Alliant system, our results show a factor of 30 times higher performance in Mflops. Multicoloring transforms sparse matrices into ones with a <it>diagonal diagonal block</it> (DDB) structure, enabling parallel LU decomposition in solving PDE problems. The multicoloring technique can also be extended to solve other scientific problems characterized by sparse matrices.</p>
Parallel processing, conjugate gradient methods, multicoloring, sparse matrix, PDE solvers, memory access conflicts, cache saturation, multiprocessor performance.

K. Hwang and H. Wang, "Multicoloring of Grid-Structured PDE Solvers on Shared-Memory Multiprocessors," in IEEE Transactions on Parallel & Distributed Systems, vol. 6, no. , pp. 1195-1205, 1995.
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