JUNE 25, 2024 by Holly Auten, Lawrence Livermore National Laboratory
Collected at: https://techxplore.com/news/2024-06-matrix-unloaded-graphics-processor-boosted.html
Underlying all complex multiphysics simulations are even more complex mathematical algorithms that solve the equations describing movement of physical phenomena—for instance, the radiation diffusion and burning plasma processes inherent in fusion reactions. Preconditioned solvers are often used in these algorithms to transform a problem so that it quickly and accurately converges to a solution.
A recent paper in SIAM Journal on Scientific Computing introduces specialized solvers optimized for simulations running on graphics processing unit (GPU)–based supercomputers. The authors are computational mathematician Tzanio Kolev from LLNL’s Center for Applied Scientific Computing alongside Will Pazner and Panayot Vassilevski, both former Livermore researchers now at Portland State University.
The team’s algorithms solve the radiation diffusion equations underpinning MARBL, a mission-critical hydrodynamics code that is now running on GPUs like those in Livermore’s forthcoming exascale supercomputer El Capitan.
“MARBL’s radiation diffusion model requires a specific type of solver in order to realize GPU efficiency. Our algorithms are optimized for this scenario,” said Pazner, the paper’s lead author.
Breaking down the math
For faster computation, these solvers subdivide physical systems into a finite element space known as H(div). This type of discretization increases the number of finite elements while decreasing their size—think of a 3D mesh breaking down into smaller and smaller squares while retaining a constant overall volume.
H(div) is one of four finite element spaces in a set of differential equations known as the de Rham complex, all of which are incorporated into the Livermore-led MFEM (Modular Finite Element Methods) software library.
“Throughout our research, we’d developed efficient solvers for the other spaces but hadn’t quite figured out the best way to approach H(div). We knew we could do better,” Koley said.
Many finite elements are calculated with matrices, in which numbers are arranged into rows and columns. But when a matrix is too large to compute with efficiency—such as in large-scale hydrodynamics simulations—matrix-free algorithms provide the only feasible way forward.
“High-order problems become prohibitively expensive to run on GPUs, not just because of the size to compute but also the memory transfer. Matrix-based algorithms often cause memory bottlenecks on GPUs, so we needed to come up with algorithms that arrive at the same solution while avoiding the processing and storing of the matrix,” Pazner said.
Though armed with matrix-free de Rham solvers, the team was still missing something to lighten H(div)’s computational burden. The turning point was their decision to reformulate the problem in the context of a saddle-point system, whose surfaces resemble a riding saddle when plotted on a graph. In essence, they turned the original problem into two coupled problems with a different mathematical structure.
The saddle-point technique may seem counterintuitive, but as the researchers tried other techniques, this one eventually rose to the top.
“Recasting the problem into a saddle-point formulation expands the original condensed, tightly packed formulation,” Pazner said. “The structure becomes bigger but also much more amenable to solution.”
The team’s innovative H(div) solvers efficiently combine all of the above approaches. “Looking at the problem in saddle-point form enabled us to take advantage of the matrix-free structure in a favorable way,” Kolev said, crediting his co-authors with the breakthrough.
“H(div) was the last tool in the de Rham toolbox for our users. We’d been considering this problem for a long time, and now we have state-of-the-art methods in all of the de Rham spaces.”
More information: Will Pazner et al, Matrix-Free High-Performance Saddle-Point Solvers for High-Order Problems in \(\boldsymbol{H}(\operatorname{\textbf{div}})\), SIAM Journal on Scientific Computing (2024). DOI: 10.1137/23M1568806
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