Extreme-Scale Computational Science Discovery in Fluid Dynamics and Related Disciplines

11:30 am – 2:30 pm, Monday March 17 // Session MAR-B46 //Anaheim Convention Center, 262C (Level 2)

Chair:: Daniel Livescu, Los Alamos National Laboratory (LANL); Pui-Kuen Yeung, Georgia Institute of Technology
Topics:
Sponsored by: DCOMP
DFD

Parallel and GPU-optimized linear solver for compact difference schemes

12:30 pm – 12:42 pm

Presenter: Hang Song (Stanford University)

Authors: Akshay Subramaniam (NVIDIA Corporation), Britton Olson (Lawrence Livermore National Laboratory), Andy Wu (Stanford University), Anjini Chandra (Stanford University), Spencer Bryngelson (Georgia Institute of Technology), Sanjiva Lele (Stanford University)

Compact finite difference methods are widely used for high-resolution simulations in many disciplines. The numerical method requires solving a cyclic tridiagonal or penta-diagonal system. For extreme-scale simulations, it is challenging to apply compact finite difference methods in a computationally-efficient way, particularly on devices with limited shared memory. Recently, a parallel linear solver algorithm for this purpose was developed and efficiently uses the capability of many-GPU distributed systems.

The presented work emphasizes algorithmic and implementation optimization strategies. The efforts are focused on achieving both scalability and absolute throughput. With this motivation, an open-source linear solver package is introduced to solve the linear systems arising from compact numerical schemes. The linear solver is implemented in the "MPI+X" paradigm, supporting various parallel processing units with portable performance. A set of uniform application programming interfaces (APIs) are provided. The solution process supports a partitioned 3D structured mesh. Raw pointers can pass the necessary data, providing compatibility with existing user code.

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