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Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alya's main features and focus particularly on its solvers. We present Alya's performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence. [ABSTRACT FROM AUTHOR]

Abstract: We present here the enhancement of a parallel incompressible Navier–Stokes solver to be able to manage very large meshes. Mesh generation in engineering applications is often the bottleneck of the complete simulation process. The mesh is the basis of the discretization algorithm and the first “lego” of a simulation. A mesh should approximate well the necessary geometrical elements of the computational domain. In addition, it should be fine enough to capture the relevant physical scales of the engineering problem. Usually, commercial mesh generators do well with the first task. They include refinement tools for boundary layer elements and local adaptivity. However, it is quite difficult to generate very large meshes (say of the order of thousands of millions of elements) with the available tools. The idea of this work is to implement a parallel uniform mesh multiplication in a HPC code developed at Barcelona Supercomputing Center named Alya. [Copyright &y& Elsevier]

The complexity of combustion simulations demands the latest high-performance computing tools to accelerate its time-to-solution results. A current trend on HPC systems is the utilization of CPUs with SIMD or vector extensions to exploit data parallelism. Our work proposes a strategy to improve the automatic vectorization of finite-element-based scientific codes. The approach applies a parametric configuration to the data structures to help the compiler detect the block of codes that can take advantage of vector computation while maintaining the code portable. A detailed analysis of the computational impact of this methodology on the different stages of a CFD solver is studied on the PRECCINSTA burner simulation. Our parametric implementation has proven to help the compiler generate more vector instructions in the assembly operation: this results in a reduction of up to 9. 39 × of the total executed instruction maintaining constant the Instructions Per Cycle and the CPU frequency. The proposed strategy improves the performance of the CFD case under study up to 4. 67 × on the MareNostrum 4 supercomputer. • A parametric configuration of the CFD data structures is presented for enabling auto-vectorization. • A detailed performance analysis using different compilers is applied to a real CFD case. • A reduction of up to 9.39 times of the total executed instruction is attained, maintaining constant the IPC and the CPU frequency. • The proposed strategy accelerates up to 4.67 the PRECCINSTA burner simulation on the Marenostrum4 nodes. [ABSTRACT FROM AUTHOR]