Strategies for adopting FVTD on multicomputers [finite-volume time-domain analysis]

Shang, J. S.; Wagner, Marcus; Pan, Yulong; Blake, Douglas

doi:10.1109/5992.814651

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…For comparison purposes, we also include the speedups of the same code running on an IBM SP2 machine using MPI in Figure 3. The MPI code uses a three-dimensional decomposition scheme [13] and hence the amount of data exchange is smaller than with the HPF code which basically uses onedimensional decomposition (parallelizing one loop only). Because of the different architectures and memory sizes, it is hard to compare the execution times of the T3E and IBM SP2.…”

Section: Performance Resultsmentioning

confidence: 99%

“…One is the sequential Fortran version called MAX3D developed by the second author of this paper for vector machines such as the CRAY C90. The second is the MPI (Message-passing Interface) [11,12] version implemented by Dr Marcus Wagner of IBM [13]. The third one is the Power Fortran Accelerator (PA) [14] version developed by the first author when he was an Air Force Office for Scientific Research (AFOSR) Faculty Research Fellow at the Wright Patterson Air Force Base.…”

Section: Introductionmentioning

confidence: 99%

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A scalable HPF implementation of a finite‐volume computational electromagnetics application on a CRAY T3E parallel system

Pan

Shang

Guo

2003

Concurrency and Computation

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SUMMARYThe time-dependent Maxwell equations are one of the most important approaches to describing dynamic or wide-band frequency electromagnetic phenomena. A sequential finite-volume, characteristic-based procedure for solving the time-dependent, three-dimensional Maxwell equations has been successfully implemented in Fortran before. Due to its need for a large memory space and high demand on CPU time, it is impossible to test the code for a large array. Hence, it is essential to implement the code on a parallel computing system. In this paper, we discuss an efficient and scalable parallelization of the sequential Fortran time-dependent Maxwell equations solver using High Performance Fortran (HPF). The background to the project, the theory behind the efficiency being achieved, the parallelization methodologies employed and the experimental results obtained on the Cray T3E massively parallel computing system will be described in detail. Experimental runs show that the execution time is reduced drastically through parallel computing. The code is scalable up to 98 processors on the Cray T3E and has a performance similar to that of an MPI implementation. Based on the experimentation carried out in this research, we believe that a high-level parallel programming language such as HPF is a fast, viable and economical approach to parallelizing many existing sequential codes which exhibit a lot of parallelism.

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Section: Performance Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A scalable HPF implementation of a finite‐volume computational electromagnetics application on a CRAY T3E parallel system

Pan

Shang

Guo

2003

Concurrency and Computation

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“…In the early 2000's, the data processing rate had reached a value of one GFLOPS (Giga floating point operation per second, 10 9 FLOPS) and the size of distributed memory has approached a terabyte. From basic research result, the load balancing, communication minimization among processors, and efficient cash memory utilization were found to be paramount for high parallel numerical simulation efficiency [67]. The versatility of unstructured Navier-Stokes/Euler solvers has been demonstrated for aircraft design.…”

Section: Landmark Achievementsmentioning

confidence: 99%

Landmarks and new frontiers of computational fluid dynamics

Shang

2019

Adv. Aerodyn.

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A narrative of landmarks in computational fluid dynamics (CFD) is presented to highlight the cornerstone achievements. Illuminating accomplishments starting from the very beginning of the coherent development until the most recent progress will be elucidated over the span over more than six decades. Meanwhile, the cuttingedge scientific innovations will also be discussed for their lasting impacts to fluid dynamics and the physics-based modeling and simulation discipline. To traverse such a vast domain over time by a single presentation, numerous and excellent contributions to CFD will be unavoidably overlooked. Nevertheless it is my ardent hope that the present discussion will be able to reaffirm excellence in research and to identify new frontiers for scientific research. Especially, the challenges to future innovations will also be delineated to recommend for potential and fertile research areas for the modeling and simulation science.

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“…The collective modeling and simulation tools span a range, including the asymptotic method, the frequency-domain method (or method of moments, MoM), the time-domain method, and the more recent hybrid technique [471,535,647,857]. Individually, they may be limited either by predictive accuracy or by practicality in application.…”

Section: Performancementioning

confidence: 99%

“…First, concurrent computing significantly reduces the wall-clock time required for data processing. Recent research efforts in porting CEM programs to multicomputers have recorded two orders of magnitude of speedup in the data processing rate [857].…”

Section: Performancementioning

confidence: 99%