Phase modeling of a parallel scientific code

Worley, Patrick H

doi:10.1109/shpcc.1992.232677

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In earlier research, we showed that different logical phases of a code may need to be modeled individually [30]. Each phase has its own computation rate, depending on the amount of computation and the amount and pattern of memory accesses.…”

Section: Modeling Pstswmmentioning

confidence: 99%

Performance modeling for SPMD message-passing programs

Brehm

Worley

Madhukar

1998

Concurrency: Pract. Exper.

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Today's massively parallel machines are typically message-passing systems consisting of hundreds or thousands of processors. Implementing parallel applications efficiently in this environment is a challenging task, and poor parallel design decisions can be expensive to correct. Tools and techniques that allow the fast and accurate evaluation of different parallelization strategies would significantly improve the productivity of application developers and increase throughput on pardel architectures. This paper investigates one of the major issues in building tools to compare parallelization strategies: determining what type of performance models of the application code and of the computer system are sufficient for a fast and accurate comparison of different strategies. The paper is built around a case study employing the Performance Prediction Tool (PerPreT) to predict performance of the Parallel Spectral Transform Shallow Water Model code (PSTSWM) on the Intel Paragon.PSTSWM is a parallel application code that was designed to evaluate different pard e l strategies for the spectral transform method as it is used in climate modeling and weather forecasting. Multiple parallel algorithms and algorithm variants are embedded in the code. PerPreT uses a relatively simple algebraic model to predict execution time for SPMD (Single Program Multiple Data) parallel applications. Applications are modeled through parameterized formulae for communication and computation, where the parameters include the problem size, the number of processors used to execute the program, and system characteristics (e+, setup times for communication, link bandwidth, and sustained computing performance per processor).In this paper we describe performance models that predict the performance of the different algorithms in PSTSWM accurately enough to allow them to be compared, establishing the feasibility of such a demanding application of performance modeling. We also discuss issues in generating and validating the performance models, emphasizing the practical importance of tools such as PerPreT in such studies.

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Section: Modeling Pstswmmentioning

confidence: 99%