Presentation
AmgT: Algebraic Multigrid Solver on Tensor Cores
DescriptionAlgebraic multigrid (AMG) methods are efficient to solve diverse sparse linear systems, due to their flexibility and adaptability. Even though modern parallel devices brought massive parallelism to AMG, the latest major hardware tensor core and their low-precision compute power, have not been exploited to accelerate AMG.
This paper proposes AmgT, a new AMG solver that utilizes the tensor core and mixed-precision ability. Considering that the SpGEMM and SpMV are extensively used in the setup and solve phases, respectively, we propose a novel method based on a unified storage format that leverages tensor cores and their variable precision. To utilize algorithm components in existing libraries, the data format and compute kernels of the AmgT solver are incorporated into the Hypre. The experimental results on NVIDIA A100 and H100 GPUs show that our AmgT outperforms the GPU version of Hypre by a factor of on average 1.46× and 1.32×, respectively.
This paper proposes AmgT, a new AMG solver that utilizes the tensor core and mixed-precision ability. Considering that the SpGEMM and SpMV are extensively used in the setup and solve phases, respectively, we propose a novel method based on a unified storage format that leverages tensor cores and their variable precision. To utilize algorithm components in existing libraries, the data format and compute kernels of the AmgT solver are incorporated into the Hypre. The experimental results on NVIDIA A100 and H100 GPUs show that our AmgT outperforms the GPU version of Hypre by a factor of on average 1.46× and 1.32×, respectively.
Event Type
Paper
TimeWednesday, 20 November 20241:30pm - 2pm EST
LocationB312-B313A
Accelerators
Algorithms
Data Compression
Linear Algebra
Tensors
TP
Best Paper Finalist
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