| 1 |
At the most abstract level, conjugate gradient has minimal parallelism
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u, r, a can be updated independently
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| 2 |
The real potential parallelism is in the matrix and vector operations, however.
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r × r is a reduction of size N
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u + a p is a vector update of size N
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A * p is a (sparse) matrix-vector multiply, in this case of size O(N)
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It looks a lot like the operator in Jacobi
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| 3 |
Conclusions:
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Stick to the matrix/vector operators
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Task parallelism (and pipelining) may improve some
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