Towards Extreme-Scale Simulations with Next-Generation Trilinos: A Low Mach Fluid Application Case Study

Trilinos is an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. While the original version of Trilinos was designed for highly scalable solutions for large problems, the need for increasingly higher fidelity simulations has pushed the problem sizes beyond what could have been envisioned two decades ago. When problem sizes exceed a billion elements even highly scalable applications and solver stacks require a complete revision. The next-generation Trilinos employs C++ templates in order to solve arbitrarily large problems and enable extreme-scale simulations. We present a case study that involves integration of Trilinos with an engineering application (Sierra low Mach module/Nalu), involving the simulation of low Mach fluid flow for problems of size up to nine billion elements. Through the use of improved algorithms and better software engineering practices, we demonstrate good weak scaling for the matrix assembly and solve for the engineering application for up to a nine billion element fluid flow large eddy simulation (LES) problem on unstructured meshes with a 27 billion row matrix on 131,072 cores of a Cray XE6 platform.

[1]  Y. Saad,et al.  GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems , 1986 .

[2]  Jonathan Joseph Hu,et al.  Design considerations for a flexible multigrid preconditioning library , 2012 .

[3]  Mehmet Deveci,et al.  Multi-jagged: A Scalable Multi-section based Spatial Partitioning Algorithm. , 2013 .

[4]  Jonathan J. Hu,et al.  ML 5.0 Smoothed Aggregation Users's Guide , 2006 .

[5]  Daniel Sunderland,et al.  Manycore performance-portability: Kokkos multidimensional array library , 2012, Sci. Program..

[6]  Alan B. Williams,et al.  toolkit computational mesh conceptual model. , 2010 .

[7]  Walid Chakroun,et al.  LDA measurements in the turbulent round jet , 1997 .

[8]  Stefan P. Domino,et al.  SIERRA/Fuego: A Multi-Mechanics Fire Environment Simulation Tool , 2003 .

[9]  Martin Berzins,et al.  Large Scale Parallel Solution of Incompressible Flow Problems Using Uintah and Hypre , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[10]  Courtenay T. Vaughan,et al.  Zoltan data management services for parallel dynamic applications , 2002, Comput. Sci. Eng..

[11]  John N. Shadid,et al.  Aztec user`s guide. Version 1 , 1995 .

[12]  Sivasankaran Rajamanickam,et al.  Amesos2 and Belos: Direct and iterative solvers for large sparse linear systems , 2012, Sci. Program..

[13]  Vincent Moureau,et al.  From Large-Eddy Simulation to Direct Numerical Simulation of a lean premixed swirl flame: Filtered laminar flame-PDF modeling , 2011 .

[14]  Tamara G. Kolda,et al.  An overview of the Trilinos project , 2005, TOMS.

[15]  Alan R. Kerstein,et al.  A petascale direct numerical simulation study of the modelling of flame wrinkling for large-eddy simulations in intense turbulence , 2012 .

[16]  Michael A. Heroux,et al.  Tpetra, and the use of generic programming in scientific computing , 2012 .