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SCHEDULE: NOV 15-20, 2015

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High-Performance Algebraic Multigrid Solver Optimized for Multi-Core Based Distributed Parallel Systems

SESSION: Linear Algebra


EVENT TAG(S): Algorithms, Scientific Computing, Solvers

TIME: 4:00PM - 4:30PM

SESSION CHAIR(S): Gabriel Tanase

AUTHOR(S):Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang, Dheevatsa Mudigere, Pradeep Dubey



Algebraic multigrid (AMG) is a linear solver, well known for its linear computational complexity and excellent scalability. As a result, AMG is expected to be a solver of choice for emerging extreme scale systems. While node level performance of AMG is generally limited by memory bandwidth, achieving high bandwidth efficiency is challenging due to highly sparse irregular computation, such as triple sparse matrix products, sparse-matrix dense-vector multiplications, independent set coarsening algorithms, and smoothers such as Gauss-Seidel. We develop and analyze a highly optimized AMG implementation, based on the well-known HYPRE library. Compared to the HYPRE baseline implementation, our optimized implementation achieves 2.0x speedup on a recent Intel Haswell Xeon processor. Combined with our other multi-node optimizations, this translates into up even higher speedups when weak-scaled to multiple nodes. In addition, our implementation achieves 1.3x speedup compared to AmgX, NVIDIA's high-performance implementation of AMG, running on K40c.

Chair/Author Details:

Gabriel Tanase (Chair) - IBM Corporation|

Jongsoo Park - Intel Corporation

Mikhail Smelyanskiy - Intel Corporation

Ulrike Meier Yang - Lawrence Livermore National Laboratory

Dheevatsa Mudigere - Intel Corporation

Pradeep Dubey - Intel Corporation

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society