Euro-Par 2009 Parallel Processing

Volume 5704 of the series Lecture Notes in Computer Science pp 797-808

PSPIKE: A Parallel Hybrid Sparse Linear System Solver

  • Murat ManguogluAffiliated withDepartment of Computer Science, Purdue University
  • , Ahmed H. SamehAffiliated withDepartment of Computer Science, Purdue University
  • , Olaf SchenkAffiliated withComputer Science Department, University of Basel

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The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, we present a new parallel solver that combines the desirable characteristics of direct methods (robustness) and effective iterative solvers (low computational cost), while alleviating their drawbacks (memory requirements, lack of robustness). Our proposed hybrid solver is based on the general sparse solver PARDISO, and the “Spike” family of hybrid solvers. The resulting algorithm, called PSPIKE, is as robust as direct solvers, more reliable than classical preconditioned Krylov subspace methods, and much more scalable than direct sparse solvers. We support our performance and parallel scalability claims using detailed experimental studies and comparison with direct solvers, as well as classical preconditioned Krylov methods.


Hybrid Solvers Direct Solvers Krylov Subspace Methods Sparse Linear Systems