# FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG

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1 FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG INSTITUT FÜR INFORMATIK (MATHEMATISCHE MASCHINEN UND DATENVERARBEITUNG) Lehrstuhl für Informatik 10 (Systemsimulation) Massively Parallel Multilevel Finite Element Solvers on the Altix 4700 Tobias Gradl, Ulrich Rüde Technical Report 07-12

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4 Before we turn to the techniques that make this possible, we would like to comment on why we do not use domain decomposition methods that might be suggested as an alternative to using multigrid. In particular, it may be argued that it is easier to implement parallel domain decomposition efficiently than parallel multigrid, since it avoids the need of a grid hierarchy. However, the price for the ease of implementation is a deterioration of the convergence rate that makes plain domain decomposition methods unsuitable on massively parallel systems. Of course, they can be improved by using an auxiliary coarse space within each iteration of the algorithm. In our view, however, with a coarse space, domain decomposition methods lose much of their advantage, since they will suffer from essentially the same bottleneck as multigrid. We wish to point out that the need for global communication is a fundamental feature of elliptic PDEs and so there is no hope to get around it by any algorithm. Multigrid methods seem to use the minimal amount of computation and also the minimal amount of communication that is necessary to solve the problem. Using a hierarchical mesh structure is essential not only to keep the operation count optimal, but also to keep for the amount of communication minimal. The difficulty is to organize and implement this efficiently. Using a mesh hierarchy can only be avoided if one is willing to pay by using more iterations. In total this may therefore lead to even more communication. We believe that the computational results below demonstrate that multigrid is the method of choice for solving extremely large PDE problems in parallel. Hierarchical Hybrid Grids HHG ("Hierarchical Hybrid Grids") [1,2,3] is a framework for the multigrid solution for finite element (FE) problems. FE methods are often preferred for solving elliptic PDEs, since they permit flexible, unstructured meshes. Among the multigrid methods, algebraic multigrid also supports unstructured grids automatically. Geometric multigrid, in contrast, relies on a given hierarchy of nested grids. On the other hand, geometric multigrid achieves a significantly higher performance in terms of unknowns computed per second. HHG is designed to close this gap between FE flexibility and geometric multigrid performance by using a compromise between structured and unstructured grids: a coarse input FE mesh is organized into the grid primitives: vertices, edges, faces, and volumes that are then refined in a structured way as indicated in Fig 1. This approach preserves the flexibility of unstructured meshes, while the regular internal structure allows for an efficient implementation on current computer architectures, especially on parallel computers. Figure 1: Regular refinement example for a two-dimensional input grid. Beginning with the input grid on the left, each successive level of refinement creates a new grid that has a larger number of interior points with structured couplings. Parallelization To exploit high end computers, the programs must be parallelized using message passing. The HHG framework is an ideal starting point for this, since the mesh partitioning can be essentially accomplished on the level of the coarse input grid, that is, with a grid size that

5 can still be handled efficiently by standard mesh partitioning software like Metis. Figure 2a shows a simple 2D example of such a grid distribution. Two triangular elements are assigned to the two processes P 0 and P 1. The unknowns on the edge between the elements are coupled to both elements and are thus needed by both processes. This introduces communication (Fig 2b) and is equivalent to using ghost nodes, as is typical in parallel mesh algorithms. The edge data structure itself can be assigned to any one of the two processors. (a) Figure 2: Grid distribution among processes. The encircled nodes are ghost values. In order to avoid excessive latency, the algorithmic details and communication must be designed carefully. The multigrid solver uses a Gauß Seidel smoother that traverses the grid points in the order of the primitives of the coarse input mesh: vertices edges faces volumes. During the update of any such group, no parallel communication is necessary, because a vertex, for example, can only be connected to another vertex indirectly via an edge. This means that, rather than sending many small messages, each type of primitive can have its parallel dependencies updated as a single large message, which greatly reduces communication latency. However, a true Gauß Seidel sweep traversing over the grid points still requires too many communication steps during each iteration, since neighboring grid points might belong to different processes. The current HHG implementation ignores a few such dependencies, thus giving the smoother the characteristics of a Jacobi iteration at the affected points. Numerically, this leads to a slight deterioration of the convergence rate, but the reduction in execution speed more than outweighs this effect. World record in linear system solving In our lagest computation to date, we have used 9170 cores of HLRB II and HHG to solve a finite element problem with 307 billion unknowns in 93 seconds run time. The problem itself is artificially designed by meshing a cube. This is necessary to ease the construction of problems with varying mesh size for our scalability study. However, the HHG data structures would allow for an arbitrary tetrahedral input mesh. We believe that this is the largest finite element system that has been solved to date. Additionally, we point out that the absolute times to solution are still fast enough to leave room for using this solver as a building block in e.g. a time stepping scheme. The results in Table 1 additionally show the results of a scaling experiment from 4 to 9170 compute cores. The amount of memory per core is kept constant and the problem size is chosen to fill as much of the available memory as possible. If the program were perfectly scalable, the average time per V cycle would stay constant throughout the table, because the ratio of problem size (i.e. workload) versus number of cores (i.e. compute power) stays constant. Near perfect scaling is seen as measure of the quality of an algorithm and its implementation. For the HLRB II in installation phase 1 the computation time increases only by a factor of 2.2 when scaling from 4 to 3825 cores. This is still not per (b)

6 fect but in our view acceptable, especially when compared to other algorithms and especially in terms of the absolute compute time. Note that perfect scalability is the more difficult to achieve the faster a code is. While every core of HLRB II phase 1 still had its own memory and network interface, the new dual core configuration provides less bandwidth per core since two cores must share an interface. Additionally, a part of the installation is now configured as so called high density partitions where two dual core processors and thus four cores share one interface. Benchmark results including these high density partitions are marked with an asterisk in table 1. The timings for 64, 504 and 2040 cores show that the dual core processors of phase 2 account for approximately 39% deterioration in runtime compared to phase 1. Scaling on the regular partitions shows a runtime increase from 4.93 s on 64 cores to 6.33 s on 6120 cores. On the high density partitions, the runtime deteriorates to 7.06 s on just 128 cores but then increases only slightly further to 7.75 sec for our largest runs. Number of cores Number of unknowns ( 10 6 ) Average time per V cycle (sec) Time to solution ( r < 10 6 r 0 ) Phase 1 Phase 2 Phase 1 Phase * * * * * * 32 1, * * 64 2, , * * 252 8, * * , , , , , , * 89.2 * , * 93.0 * Table 1: Scaleup results for HHG. With a convergence rate of 0.3, 12 V-cycles are necessary to reduce the starting residual by a factor of The entries marked with * correspond to runs on (or including) high density partitions with reduced memory bandwidth per core. Conclusions The HHG framework and the HLRB II have been used to solve a finite element problem of world record size, exceeding previous results by more than an order of magnitude, see [2,5]. HHG draws its power from using a multigrid solver that is especially designed and carefully optimized for current, massively parallel high performance architectures. The SGI Altix architecture is found to be well suited for large scale iterative FE solvers. References [1] B. Bergen, T. Gradl, F. Hülsemann, U. Rüde. A Massively Parallel Multigrid Method for Finite Elements, Computing in Science and Engineering 8:56 62, 2006.

7 [2] B. Bergen, F. Hülsemann, U. Rüde: Is 1.7 x Unknowns the Largest Finite Element System that Can Be Solved Today? Proceedings of the ACM/IEEE 2005 Supercomputing Conference, Seattle, Nov. 2005, ISBN [3] G. Hager, B. Bergen, P. Lammers, G. Wellein. Taming the Bandwidth Behemoth. First Experiences on a Large SGI Altix System. In: inside, 3(2):24, [4] B. Bergen, G. Wellein, F. Hülsemann, U. Rüde: Hierarchical Hybrid Grids Achieving TERAFLOP Performance on Large Scale Finite Element Simulation, International Journal of Parallel, Emergent and Distributed Systems, 22(4): , (5) M.F. Adams, H.H Bayraktar, T.M. Keaveny, P. Papadopoulos: Ultrascalable implicit finite element analyses in solid mechanics with over a half a billion degrees of freedom, ACM/IEEE Proceedings of SC2004: High Performance Networking and Computing, 2004.

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