1,180 research outputs found
HPCCP/CAS Workshop Proceedings 1998
This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey
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Parallelization and I/O performance optimization of a global nonhydrostatic dynamical core using MPI
The Global â Regional Integrated forecast SysTem (GRIST) is the next-
generation weather and climate integrated model dynamic framework developed by
Chinese Academy of Meteorological Sciences. In this paper, we present several changes
made to the global nonhydrostatic dynamical (GND) core, which is part of the ongoing
prototype of GRIST. The changes leveraging MPI and PnetCDF techniques were targeted
at the parallelization and performance optimization to the original serial GND core.
Meanwhile, some sophisticated data structures and interfaces were designed to adjust
flexibly the size of boundary and halo domains according to the variable accuracy in
parallel context. In addition, the I/O performance of PnetCDF decreases as the number of
MPI processes increases in our experimental environment. Especially when the number
exceeds 6000, it caused system-wide outages (SWO). Thus, a grouping solution was
proposed to overcome that issue. Several experiments were carried out on the
supercomputing platform based on Intel x86 CPUs in the National Supercomputing
Center in Wuxi. The results demonstrated that the parallel GND core based on grouping
solution achieves good strong scalability and improves the performance significantly, as
well as avoiding the SWOs
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AIMES: advanced computation and I/O methods for earth-system simulations
Dealing with extreme scale Earth-system models is challenging from the computer science perspective, as the required computing power and storage capacity are steadily increasing.
Scientists perform runs with growing resolution or aggregate results from many similar smaller-scale runs with slightly different initial conditions (the so-called ensemble runs).
In the fifth Coupled Model Intercomparison Project (CMIP5), the produced datasets require more than three Petabytes of storage and the compute and storage requirements are increasing significantly for CMIP6.
Climate scientists across the globe are developing next-generation models based on improved numerical formulation leading to grids that are discretized in alternative forms such as an icosahedral (geodesic) grid.
The developers of these models face similar problems in scaling, maintaining and optimizing code.
Performance portability and the maintainability of code are key concerns of scientists as, compared to industry projects, model code is continuously revised and extended to incorporate further levels of detail.
This leads to a rapidly growing code base that is rarely refactored.
However, code modernization is important to maintain productivity of the scientist working
with the code and for utilizing performance provided by modern and future architectures.
The need for performance optimization is motivated by the evolution of the parallel architecture landscape from
homogeneous flat machines to heterogeneous combinations of processors with deep memory hierarchy.
Notably, the rise of many-core, throughput-oriented accelerators, such as GPUs, requires non-trivial code changes at minimum and, even worse, may necessitate a substantial rewrite of the existing codebase.
At the same time, the code complexity increases the difficulty for computer scientists and vendors to understand and optimize the code for a given system.
Storing the products of climate predictions requires a large storage and archival system which is expensive.
Often, scientists restrict the number of scientific variables and write interval to keep the costs
balanced.
Compression algorithms can reduce the costs significantly but can also increase the scientific yield of simulation runs.
In the AIMES project, we addressed the key issues of programmability, computational efficiency and I/O limitations that are common in next-generation icosahedral earth-system models.
The project focused on the separation of concerns between domain scientist, computational scientists, and computer scientists
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