Multiple right hand side multigrid for domain wall fermions with a multigrid preconditioned block conjugate gradient algorithm
September 05, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Peter A Boyle
arXiv ID
2409.03904
Category
hep-lat
Cross-listed
cs.DC,
math.NA
Citations
4
Venue
arXiv.org
Last Checked
3 months ago
Abstract
We introduce a class of efficient multiple right-hand side multigrid algorithm for domain wall fermions. The simultaneous solution for a modest number of right hand sides concurrently allows for a significant reduction in the time spent solving the coarse grid operator in a multigrid preconditioner. We introduce a preconditioned block conjuate gradient with a multigrid preconditioner, giving additional algorithmic benefit from the multiple right hand sides. There is also a very significant additional to computation rate benefit to multiple right hand sides. This both increases the arithmetic intensity in the coarse space and increases the amount of work being performed in each subroutine call, leading to excellent performance on modern GPU architectures. Further, the software implementation makes use of vendor linear algebra routines (batched GEMM) that can make use of high throughput tensor hardware on recent Nvidia, AMD and Intel GPUs. The cost of the coarse space is made sub-dominant in this algorithm, and benchmarks from the Frontier supercomputer system show up to a factor of twenty speed up over the standard red-black preconditioned conjugate gradient algorithm on a large system with physical quark masses.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β hep-lat
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Lattice gauge equivariant convolutional neural networks
R.I.P.
π»
Ghosted
Aspects of scaling and scalability for flow-based sampling of lattice QCD
R.I.P.
π»
Ghosted
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
R.I.P.
π»
Ghosted
Job Management and Task Bundling
R.I.P.
π»
Ghosted
Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted