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RAMS Parallel Processing Techniques

RAMS Parallel Processing Techniques. RAMS Parallel Design Considerations. Concentrating on larger problems (only 3-dimensional) No compromises in physics and numerics Target distributed memory, MIMD architectures workstation cluster first platform

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RAMS Parallel Processing Techniques

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  1. RAMS Parallel Processing Techniques

  2. RAMS Parallel Design Considerations • Concentrating on larger problems (only 3-dimensional) • No compromises in physics and numerics • Target distributed memory, MIMD architectures • workstation cluster first platform • Single code version for single processor and parallel platforms • No performance degradation on single processor platforms • Dynamic load balancing needed

  3. RAMS Parallel Components and Structure • Main structure • Memory structure • Variable tables • Domain decomposition • Communication procedures • Concurrent computation/communication • Bookkeeping arrays • File output • Nested grid considerations • Dynamic load balancing

  4. RAMS Parallel Structure • For parallel execution, serial (single-processor) code divided into two types: • Master process • Initialization • All input/output functions • Node (“compute”) processes • All computation

  5. RAMS Memory Structure • Many options / array space / nests with variable numbers of grid points • Dynamically-allocate “A” array • “1-D” array with all memory • “Pointers” to beginning of each field (integer indices in COMMON) • Computational routines dynamically-dimension arrays • Each node/master process allocates its own memory

  6. RAMS Variable Tables • Data file defines characteristics of each model array • Existence (whether on node or master processes) • Output • Communication

  7. # Velocity, perturbation Exner function UP : 1:3: 0:110000:vard:hist:anal:lite:mpti:mpt3:mpt2 UC : 2:3: 0:110000:vard:hist:mpti:mpt3 VP : 3:3: 0:110000:vard:hist:anal:lite:mpti:mpt3:mpt2 VC : 4:3: 0:110000:vard:hist:mpti:mpt3 WP : 5:3: 0:110000:vard:hist:anal:lite:mpti:mpt3:mpt2 WC : 6:3: 0:110000:vard:hist:mpti:mpt3 PP : 7:3: 0:110000:vard:hist:anal:lite:mpti:mpt3:mpt2 PC : 8:3: 0:110000:vard:hist:mpti:mpt3 RAMS VTABLE File

  8. Domain Decomposition Nodes exchange “overlap” rows (halo regions) with adjacent nodes

  9. Domain decomposition • Each column assigned a “work” factor • Factor based on boundary, physics, machine performance, etc. • Domain horizontally decomposed across processors • “2-D” decomposition (3 or more nodes)

  10. Timestep flow - single grid - serial Compute long timestep Start 1 2 3 End Small or acoustic timesteps Start

  11. Timestep flow - single grid - parallel Node 1: Start 1 1 1 End Compute Compute Node 2: Start 1 1 1 End Compute Compute

  12. Concurrent communication/computation • Overlap rows sent first thing in timestep flow. • Model processes that can be computed without overlap rows are • done first. • Sub-domain interior is computed for some terms • Messages are received • Sub-domain boundary terms are computed

  13. Bookkeeping arrays Keep track of communication “paths” source node / destination node ipaths(info,itype,ngrids,nproc,nproc) • number of processor • message bounds • machine id • grid number • “type” of message iget_paths(itype,ngrids,nproc,nproc)

  14. Communication “types” • Initialization / re-decomposition • Master to node; Full sub-domains; Most variables • Long timestep overlap region • Node to node; 1 row; Prognostic variables • Small timestep overlap region (2 events) • Node to node; 1 row; Selected variables • File output • Node to master; Full sub-domains; Prognostic variables • All node to node types package variables in a single message before sending.

  15. File output • Nodes transfer necessary data to master process • Transfer done with message passing • No local disk space or NFS needed for file output • Nodes can continue computing • Data available on master for other activities • Parallel I/O not needed

  16. Nested grid considerations • all grids decomposed independently • 2-way interactive communication • interpolation of nested grid boundaries performed on fine grid • nodes, coarse grid information sent to fine grid • feedback (averaging) of fine grid information performed on fine • grid node, averaged values sent to coarse grid • minimizes data needed to be communicated

  17. Dynamic load balancing • Regions of domain can require additional physics as model • progresses (microphysics, radiation) • Nodes send timing information back to master at end of timestep • At opportune times (coinciding with file output times), • master process will: • re-compute “work” factor • re-decomposes the domain • re-initializes the nodes

  18. Performance • Performance varies, dependent on architecture, • communication hardware, model configuration • Speedups and efficiencies • Results • 60-110% efficiency Speed up: Time to run serial code S = Time to run parallel code Efficiency: E = S/ # processors

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