Abstract

Iterative sparse linear solvers are an important class of algorithm in high performance computing, and form a crucial component of many scientific codes. As intra and inter node parallelism continues to increase rapidly, the design of new, scalable solvers which can target next generation architectures becomes increasingly important. In this work we present TeaLeaf, a recent mini-app constructed to explore design space choices for highly scalable solvers. We then use TeaLeaf to compare the standard CG algorithm with a Chebyshev Polynomially Preconditioned Conjugate Gradient (CPPCG) iterative sparse linear solver. CPPCG is a communication-avoiding algorithm, requiring less global communication than previous approaches. TeaLeaf includes support for many-core processors, such as GPUs and Xeon Phi, and we include strong-scaling results across a range of world-leading Petascale supercomputers, including Titan and Piz Daint.

@inproceedings{wrap17,
  author = {McIntosh-Smith, Simon and Martineau, Matt and Deakin, Tom and Pawelczak, Grzegorz and Gaudin, Wayne and Garrett, Paul and Liu, Wei and Smedley-Stevenson, Richard and Beckingsale, David},
  title = {{TeaLeaf: A mini-application to enable design-space explorations for iterative sparse linear solvers}},
  booktitle = {{Workshop on Representative Applications help in conjuction with IEEE International Conference on Cluster Computing (WRAp)}},
  year = {2017},
  publisher = {{IEEE}},
  doi = {10.1109/CLUSTER.2017.105},
  keywords = {Conferences and Workshops}
}