Efficient Implementation of Gaussian Belief Propagation Solver for Large Sparse Diagonally Dominant Linear Systems
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Bibliographic record
Abstract
We present an implementation-oriented algorithm for the recently developed Gaussian Belief Propagation solver that demonstrates 17× speedup over the prior algorithm for diagonally dominant matrices generated by typical Finite Elements applications. Compared to the diagonally-preconditioned conjugate gradient method, our algorithm demonstrates empirical improvements up to 6× in iteration count and speedups up to 1.8× in execution time. Also we present a new flexible scheduling scheme of the algorithm that is aimed for implementation on parallel architectures by reducing the iteration count of parallel GaBP and achieving better hardware parallelism.
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| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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