101 research outputs found
Asynchronous and Multiprecision Linear Solvers - Scalable and Fault-Tolerant Numerics for Energy Efficient High Performance Computing
Asynchronous methods minimize idle times by removing synchronization barriers, and therefore allow the efficient usage of computer systems. The implied high tolerance with respect to communication latencies improves the fault tolerance. As asynchronous methods also enable the usage of the power and energy saving mechanisms provided by the hardware, they are suitable candidates for the highly parallel and heterogeneous hardware platforms that are expected for the near future
Machine learning-aided numerical linear algebra: Convolutional neural network for the efficient preconditioner generation
Machine learning-aided numerical linear Algebra: Convolutional neural networks for the efficient preconditioner generation
Porting Batched Iterative Solvers onto Intel GPUs with SYCL
Batched linear solvers play a vital role in computational sciences,
especially in the fields of plasma physics and combustion simulations. With the
imminent deployment of the Aurora Supercomputer and other upcoming systems
equipped with Intel GPUs, there is a compelling demand to expand the
capabilities of these solvers for Intel GPU architectures. In this paper, we
present our efforts in porting and optimizing the batched iterative solvers on
Intel GPUs using the SYCL programming model. The SYCL-based implementation
exhibits impressive performance and scalability on the Intel GPU Max 1550s
(Ponte Vecchio GPUs). The solvers outperform our previous CUDA implementation
on NVIDIA H100 GPUs by an average of 2.4x for the PeleLM application inputs.
The batched solvers are ready for production use in real-world scientific
applications through the Ginkgo library.Comment: 9 pages, 8 figures, submitted to the P3HPC Workshop at SC2
Energy Efficiency of Mixed Precision Iterative Refinement Methods using Hybrid Hardware Platforms: An Evaluation of different Solver and Hardware Configurations
Mixed Precision Error Correction Methods for Linear Systems: Convergence Analysis based on Krylov Subspace Methods
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