A domain-decomposition scheme sizes subdomains to GPU shared memory to remove synchronization from sparse triangular solves, reporting 10.7x and 3.2x speedups for triangular solves and ILU0-BiCGSTAB on the AMD MI210.
Inverse-Reynolds-Dominance approach to transient fluid dynamics
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abstract
We consider the evolution equations for the bulk viscous pressure, diffusion current and shear tensor derived within second-order relativistic dissipative hydrodynamics from kinetic theory. By matching the higher order moments directly to the dissipative quantities, all terms which are of second order in the Knudsen number Kn vanish, leaving only terms of order $\mathcal{O}(\textrm{Re}^{-1} \textrm{Kn})$ and $\mathcal{O}(\textrm{Re}^{-2})$ in the relaxation equations, where $\textrm{Re}^{-1}$ is the inverse Reynolds number. We therefore refer to this scheme as the Inverse-Reynolds-Dominance (IReD) approach. The remaining (non-vanishing) transport coefficients can be obtained exclusively in terms of the inverse of the collision matrix. This procedure fixes unambiguously the relaxation times of the dissipative quantities, which are no longer related to the eigenvalues of the inverse of the collision matrix. In particular, we find that the relaxation times corresponding to higher-order moments grow as their order increases, thereby contradicting the \textit{separation of scales} paradigm. The formal (up to second order) equivalence with the standard DNMR approach is proven and the connection between the IReD transport coefficients and the usual DNMR ones is established.
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Mapping Sparse Triangular Solves to GPUs via Fine-grained Domain Decomposition
A domain-decomposition scheme sizes subdomains to GPU shared memory to remove synchronization from sparse triangular solves, reporting 10.7x and 3.2x speedups for triangular solves and ILU0-BiCGSTAB on the AMD MI210.