Parallel algorithms for the spectral transform method

Foster, Ian; Worley, P.H.

doi:10.2172/10168301

Cited by 18 publications

(21 citation statements)

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“…The model was tested on the PSTSWM (Parallel Spectral Transform Shallow Water Model) [9] application. The PSTSWM is a representative compact application that solves the nonlinear shallow water equations on a rotating sphere using the spectral transform method.…”

Section: Local Knowledge Acquisitionmentioning

confidence: 99%

Using Runtime Measurements and Historical Traces for Acquiring Knowledge in Parallel Applications

Senger

Santana

2004

Computational Science - ICCS 2004

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Abstract.A new approach for acquiring knowledge of parallel applications regarding resource usage and for searching similarity on workload traces is presented. The main goal is to improve decision making in distributed system software scheduling, towards a better usage of system resources. Resource usage patterns are defined through runtime measurements and a self-organizing neural network architecture, yielding an useful model for classifying parallel applications. By means of an instance-based algorithm, it is produced another model which searches for similarity in workload traces aiming at making predictions about some attribute of a new submitted parallel application, such as run time or memory usage. These models allow effortless knowledge updating at the occurrence of new information. The paper describes these models as well as the results obtained applying these models to acquiring knowledge in both synthetic and real applications traces.

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Section: Local Knowledge Acquisitionmentioning

confidence: 99%

Using Runtime Measurements and Historical Traces for Acquiring Knowledge in Parallel Applications

Senger

Santana

2004

Computational Science - ICCS 2004

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“…The global exchange performs essentially a pairwise block exchange where the local 3-D arrays on each processor are viewed as 1-D array of blocks (Bokhari 1991;Foster and Worley 1997). This exchange involves all-toall communication.…”

Section: Three-dimensional Field Remappingmentioning

confidence: 99%

Efficient Parallel I/O in Community Atmosphere Model (CAM)

Tseng

Ding

2008

The International Journal of High Performance Computing Applica

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Century-long global climate simulations at high resolutions generate large amounts of data in a parallel architecture. Currently, the community atmosphere model (CAM), the atmospheric component of the NCAR community climate system model (CCSM), uses sequential I/O which causes a serious bottleneck for these simulations. We describe the parallel I/O development of CAM in this paper. The parallel I/ O combines a novel remapping of 3-D arrays with the parallel netCDF library as the I/O interface. Because CAM history variables are stored in disk file in a different index order than the one in CPU resident memory because of parallel decomposition, an index reshuffle is done on the fly. Our strategy is first to remap 3-D arrays from its native decomposition to zdecomposition on a distributed architecture, and from there write data out to disk. Because z-decomposition is consistent with the last array dimension, the data transfer can occur at maximum block sizes and, therefore, achieve maximum I/ O bandwidth. We also incorporate the recently developed parallel netCDF library at Argonne/Northwestern as the collective I/O interface, which resolves a long-standing issue because netCDF data format is extensively used in climate system models. Benchmark tests are performed on several platforms using different resolutions. We test the performance of our new parallel I/O on five platforms (SP3, SP4, SP5, Cray X1E, BlueGene/L) up to 1024 processors. More than four realistic model resolutions are examined, e.g. EUL T85 (~1.4°), FV-B (2° × 2.5°), FV-C (1° × 1.25°), and FV-D (0.5° × 0.625°) resolutions. For a standard single history output of CAM 3.1 FV-D resolution run (multiple 2-D and 3-D arrays with total size 4.1 GB), our parallel I/O speeds up by a factor of 14 on IBM SP3, compared with the existing I/O; on IBM SP5, we achieve a factor of 9 speedup. The estimated time for a typical century-long simulation of FV D-resolution on IBM SP5 shows that the I/O time can be reduced from more than 8 days (wall clock) to less than 1 day for daily output. This parallel I/O is also implemented on IBM BlueGene/ L and the results are shown, whereas the existing sequential I/O fails due to memory usage limitation.

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“…The spectral transform approach has useful properties from a numerical methods point of view (avoiding aliasing, accurate differentiation) at the cost of some complexity in sequential implementation. In a parallel implementation, however, it also introduces a need for global communication [8]. PCCM2 addresses these issues by incorporating parallel spectral transform algorithms developed at Argonne and Oak Ridge National Laboratories [6,8] that support the use of several hundred or more processors, depending on model resolution.…”

Section: The Foam Atmosphere Modelmentioning

confidence: 99%

“…In a parallel implementation, however, it also introduces a need for global communication [8]. PCCM2 addresses these issues by incorporating parallel spectral transform algorithms developed at Argonne and Oak Ridge National Laboratories [6,8] that support the use of several hundred or more processors, depending on model resolution. Additional modifications involved the semi-Lagrangian representation of advection and techniques for load balancing [6].…”

Section: The Foam Atmosphere Modelmentioning

confidence: 99%

FOAM: Expanding the horizons of climate modeling

Tobis¹,

Foster²,

Schafer³

1997

Self Cite

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Abstract:We report here on a project that expands the applicability of dynamic climate modeling to very long time scales. The Fast Ocean_Atmosphere Model (FOAM) is a coupled ocean-atmosphere model that incorporates physics of interest in understanding decade to century time scale variability. It addresses the high computational cost of this endeavor with a combination of improved ocean model formulation, low atmosphere resolution, and efficient coupling. It also uses message-passing parallel processing techniques, allowing for the use of cost-effective distributed memory platforms. The resulting model runs over 6000 times faster than real time with good fidelity and has yielded significant results.

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Parallel algorithms for the spectral transform method

Cited by 18 publications

References 0 publications

Using Runtime Measurements and Historical Traces for Acquiring Knowledge in Parallel Applications

Using Runtime Measurements and Historical Traces for Acquiring Knowledge in Parallel Applications

Efficient Parallel I/O in Community Atmosphere Model (CAM)

FOAM: Expanding the horizons of climate modeling

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