University of Illinois Urbana-Champaign

Accelerating an efficient implementation of MLFMA with hererogeneous computing

Ren, Wei

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https://hdl.handle.net/2142/97882

Description

Title
Accelerating an efficient implementation of MLFMA with hererogeneous computing
Author(s)
Ren, Wei
Contributor(s)
Hwu, Wen-Mei
Issue Date
2017-12
Keyword(s)
  • multilevel fast multipole algorithm
  • MLFMA
  • PETSc
  • accelerating an efficient implementation of MLFMA with heterogeneous computing
Abstract
This thesis investigates possible optimization on an efficient implementation of the multilevel fast multipole algorithm (MLFMA), which is intended for solving integral equations for large problems. Though MLFMA is not inherently parallel due to its tree-like computational structure, if carefully optimized, it is suitable for parallelization as the throughput and computation power becomes higher on current GPU accelerators. By dividing problems into hierarchical multilevel groups, the MLFMA can be distributed to supercomputers like the Blue Waters, utilizing massive computing resources and balancing the workload. For solving large problems with stability and fast convergence rate, several different iterative solvers are written using PETSc (Portable, Extensible Toolkit for Scientific Computation) math library routines in the MLFMA and compared for performance. The use of GPU accelerators has also been implemented in CUDA C++ and showed great improvement on Blue Waters.
Type of Resource
text
Language
en
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http://hdl.handle.net/2142/97882

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