Tag: multi-GPU
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JaxMG: Scalable Multi-GPU Linear Solves Beyond Memory
Unlocking Large-Dense Linear Solves with JaxMG Solving large, dense linear systems is a fundamental challenge in modern scientific computing. Traditional approaches often hit a brick wall when the problem size exceeds the memory capacity of a single GPU. Researchers led by Roeland Wiersema of the Center for Computational Quantum Physics, Flatiron Institute, are tackling this…
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Jaxmg Enables Scalable Multi-GPU Linear Solves Beyond Limits
Overview: A breakthrough in scalable linear algebra Solving large, dense linear systems is a foundational task in modern scientific computation, from quantum physics to data-intensive simulations. A persistent bottleneck has been the memory and compute constraints of individual GPUs, which restricts the size of solvable problems. In a recent development, Roeland Wiersema of the Center…
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Jaxmg: Scaling Multi-GPU Linear Solves Beyond Single-GPU Limits
Introduction: Pushing the Boundaries of Dense Linear Solves Researchers are tackling a long-standing bottleneck in scientific computing: solving large, dense linear systems efficiently. Traditional single-GPU solvers often hit memory and performance walls as problem sizes grow. A recent advance from Roeland Wiersema of the Center for Computational Quantum Physics at the Flatiron Institute, and his…