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PanguLU: A Scalable Regular Two-Dimensional Block-Cyclic Sparse Direct Solver on Distributed Heterogeneous Systems
SessionLinear Algebra I
DescriptionSparse direct solvers play a vital role in large-scale high performance computing in science and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal patterns to aggregate columns of nearly identical forms and to exploit dense basic linear algebra subprograms (BLAS) for computation. We propose a new sparse direct solver called PanguLU. Our work relies on simpler regular 2D blocking and stores blocks in their sparse forms to avoid any extra fill-ins. Based on sparse patterns of blocks, a variety of block-wise sparse BLAS methods are developed and selected for higher efficiency on local GPUs. To make PanguLU more scalable, we also adjust mapping of blocks to processes for overall more balanced workload, and propose a synchronization-free communication strategy to reduce overall latency overhead. Experiments on two distributed heterogeneous platforms consisting of 128 A100 GPUs and 128 MI50 GPUs demonstrate that PanguLU achieves up to 11.70x and 17.97x speedups over SuperLU_DIST.
Authors
Event Type
Paper
TimeWednesday, 15 November 202311:30am - 12pm MST
Location401-402
Accelerators
Algorithms
Linear Algebra
TP
Best Paper Finalist
