System implementation for US Air Force Global Theater Weather Analysis and Prediction System (GTWAPS)

The Global Theater Weather Analysis and Prediction System (GTWAPS) is intended to provide war fighters and decision makers with timely, accurate, and tailored meteorological and oceanographic (METOC) information to enhance effective employment of battlefield forces. Of critical importance to providing METOC theater information is the generation of meteorological parameters produced by numerical prediction models and application software at the Air Force Global Weather Central (AFGWC), Offutt Air Force Base, Nebraska. Ultimately, application-derived data will be produced by the regional Joint METOC Forecast Units and by the deployed teams within a theater. The USAF Air Staff contracted with Argonne National Laboratory (ANL) for assistance in defining a hardware and software solution using off-the-shelf technology that would give the USAF the flexibility of testing various meteorological models and the ability to use the system within their daily operational constraints

The Same-Source Parallel MM5

With the March 1998 release of the Penn State University/NCAR Mesoscale Model (MM5), the official version of the model (MM5v2 Release 8) now runs on distributed memory (DM) message-passing platforms. Under an IBM-funded effort, source translation and runtime library support minimize the impact of parallelization on the original model source code with the result that the majority of code is line-for-line identical with the original version. Parallel performance and scaling are equivalent to earlier, hand-parallelized versions; the modifications have no effect when the code is compiled and run without the DM option. Supported computers include the IBM SP2, Cray T3E, and Fujitsu VPP. The approach is compatible with sharedmemory parallelism, allowing DM/SM hybrid parallelization on distributed memory clusters of SMP. Preliminary results show that scalability on distributed shared memory computers such as the SGI Origin 2000 also benefits from a distributed memory programming mode..

Runtime System Library for Parallel Finite Difference Models with Nesting

RSL is a parallel run-time system library for implementing regular-grid models with nesting on distributed memory parallel computers. RSL provides support for automatically decomposing multiple model domains and for redistributing work between processors at run time for dynamic load balancing. A unique feature of RSL is that processor subdomains need not be rectangular patches; rather, grid points are independently allocated to processors, allowing more precisely balanced allocation of work to processors. Communication mechanisms are tailored to the application: RSL provides an efficient high-level stencil exchange operation for updating subdomain ghost areas and interdomain communication to support two-way interaction between nest levels. RSL also provides run-time support for local iteration over subdomains, global-local index translation, and distributed I/O from ordinary Fortran record-blocked data sets. The interface to RSL supports Fortran77 and Fortran90. RSL has been used to parallelize the NCAR/Penn State Mesoscale Model (MM5)

A Runtime System Library for Parallel Finite Difference Models with Nesting

RSL is a parallel run-time system library for implementing regular-grid models with nesting on distributed memory parallel computers. RSL provides support for automatically decomposing multiple model domains and for redistributing work between processors at run time for dynamic load balancing. A unique feature of RSL is that processor subdomains need not be rectangular patches; rather, grid points are independently allocated to processors, allowing more precisely balanced allocation of work to processors. Communication mechanisms are tailored to the application: RSL provides an efficient high-level stencil exchange operation for updating subdomain ghost areas and interdomain communication to support two-way interaction between nest levels. RSL also provides run-time support for local iteration over subdomains, global-local index translation, and distributed I/O from ordinary Fortran record-blocked data sets. The interface to RSL supports Fortran77 and Fortran90. RSL has been used to pa..

The Same-Source Parallel MM5

Beginning with the March 1998 release of the Penn State University/NCAR Mesoscale Model (MM5), and continuing through eight subsequent releases up to the present, the official version has run on distributed -memory (DM) parallel computers. Source translation and runtime library support minimize the impact of parallelization on the original model source code, with the result that the majority of code is line-for-line identical with the original version. Parallel performance and scaling are equivalent to earlier, hand-parallelized versions; the modifications have no effect when the code is compiled and run without the DM option. Supported computers include the IBM SP, Cray T3E, Fujitsu VPP, Compaq Alpha clusters, and clusters of PCs (so-called Beowulf clusters). The approach also is compatible with shared-memory parallel directives, allowing distributed-memory/shared-memory hybrid parallelization on distributed-memory clusters of symmetric multiprocessors

Rsl: A Parallel Runtime System Library For Regional Atmospheric Models With Nesting

. RSL is a parallel runtime system library developed at Argonne National Laboratory that is tailored to regular-grid atmospheric models with mesh refinement in the form of two-way interacting nested grids. RSL provides high-level stencil and interdomain communication, irregular domain decomposition, automatic local/global index translation, distributed I/O, and dynamic load balancing. RSL was used with Fortran90 to parallelize a well-known and widely used regional weather model, the Penn State/NCAR Mesoscale Model. Key words. Weather modeling, parallel computing, mesh refinement, dynamic load balancing. 1. Introduction. Models of the earth's atmosphere were among the first applications for supercomputers and continue to push the limits of available resources today [3]. Dynamic models of the atmosphere are used for forecasting and climate prediction. Such models may be categorized as global and regional. Global models provide relatively low-resolution predictive capabilities and are c..

FLIC: A Translator for Same-Source Parallel Implementation of Regular Grid Applications

1 1 Introduction 1 2 Overview 2 2.1 FLIC Input and Output : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 2.1.1 Loop Statements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 2.1.2 Global and Local Indices : : : : : : : : : : : : : : : : : : : : : : : : 4 2.1.3 Boundary Tests : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.2 Iterative Scope : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 2.3 FLIC Macros : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 3 Usage 8 3.1 Obtaining FLIC : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 3.2 Command Line Arguments : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 3.3 Directives File : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 3.4 Environment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 3.5 Compiling with FLIC : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 4 Conclusio..

Rsl: A Parallel Runtime System Library For Regional Atmospheric Models With Nesting

. RSL is a parallel runtime system library developed at Argonne National Laboratory that is tailored to regular-grid atmospheric models with mesh refinement in the form of two-way interacting nested grids. RSL provides high-level stencil and interdomain communication, irregular domain decomposition, automatic local/global index translation, distributed I/O, and dynamic load balancing. RSL was used with Fortran90 to parallelize a well-known and widely used regional weather model, the Penn State/NCAR Mesoscale Model. Key words. Weather modeling, parallel computing, mesh refinement, dynamic load balancing. 1. Introduction. Models of the earth's atmosphere were among the first applications for supercomputers and continue to push the limits of available resources today [3]. Dynamic models of the atmosphere are used for forecasting and climate prediction. Such models may be categorized as global and regional. Global models provide relatively low-resolution predictive capabilities and are c..