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lect06-omp-mpi-titanium - Notes CMSC 714 Lecture 6 MPI vs...

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CMSC 714 Lecture 6 MPI vs. OpenMP and Titanium Alan Sussman 2 CMSC 714 - Alan Sussman & Jeffrey K. Hollingsworth Notes ! MPI project due next Wed., 6PM Questions on debugging MPI programs? – ask Dr. Sussman via email ! OpenMP project posted after MPI project turned in 3 CMSC 714 - Alan Sussman & Jeffrey K. Hollingsworth OpenMP + MPI ! Some applications can take advantage of both message passing and threads Questions is what to do to obtain best overall performance, without too much programming difficulty Choices are all MPI, all OpenMP, or both • For both , common option is outer loop parallelized with message passing, inner loop with directives to generate threads ! Applications studied: Hydrology – CGWAVE Computational chemistry – GAMESS Linear algebra – matrix multiplication and QR factorization Seismic processing – SPECseis95 Computational fluid dynamics – TLNS3D Computational physics - CRETIN 4 CMSC 714 - Alan Sussman & Jeffrey K. Hollingsworth Types of parallelism in the codes ! For message passing parallelism (MPI) Parametric – coarse-grained outer loop, essentially task parallel Structured domains – domain decomposition with local operations – structured and unstructured grids Direct solvers – linear algebra, lots of communication and load balancing required – message passing works well for large systems of equations ! Shared memory parallelism (OpenMP) Statically scheduled parallel loops – one large, or several smaller loops, non-nested parallel Parallel regions – merge loops into one parallel region to reduce overhead of directives Dynamic load balanced – when static scheduling leads to load imbalance from irregular task sizes
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5 CMSC 714 - Alan Sussman & Jeffrey K. Hollingsworth CGWAVE ! Finite elements - MPI parameter space evaluation at outer loop, OpenMP sparse linear equation solver in inner loops ! Speedup using 2 levels of parallelism allows modeling larger bodies of water possible in a reasonable amount of time ! Master-worker strategy for dynamic load balancing in MPI part/ component ! Solver for each component solves large sparse linear system with OpenMP to parallelize ! On SGI Origin 2000 (distributed shared memory machine), use first touch rule to migrate data for each component to the processor that uses it ! Performance results show that best performance obtained using both MPI and OpenMP, with a combination of MPI workers and OpenMP threads that depends on the problem/grid size And for load balancing, a lot fewer MPI workers than components 6 CMSC 714 - Alan Sussman & Jeffrey K. Hollingsworth GAMESS !
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