%0 Conference Proceedings
%T Demystifying Performance Predictions of Distributed FFT3D Implementations
%+ University of Delaware [Newark]
%A Orozco, Daniel
%A Garcia, Elkin
%A Pavel, Robert
%A Ayala, Orlando
%A Wang, Lian-Ping
%A Gao, Guang
%Z Part 5: Performance Modeling, Prediction, and Tuning
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 9th International Conference on Network and Parallel Computing (NPC)
%C Gwangju, South Korea
%Y James J. Park
%Y Albert Zomaya
%Y Sang-Soo Yeo
%Y Sartaj Sahni
%I Springer
%3 Network and Parallel Computing
%V LNCS-7513
%P 196-207
%8 2012-09-06
%D 2012
%R 10.1007/978-3-642-35606-3_23
%Z Computer Science [cs]Conference papers
%X This paper presents a comprehensive story of the development of simpler performance models for distributed implementations of the Fast Fourier Transform in 3 Dimensions (FFT3D). We start by providing an overview of several implementations and their performance models. Then, we present arguments to support the use of a simple power function instead of the full performance models proposed by other publications. We argue that our model can be obtained for a particular problem size with minimal experimentation while other models require significant tuning to determine their constants.Our advocacy for simpler performance models is inspired by the difficulties found when estimating the performance of FFT3D programs. Correctly estimating how well large-scale programs (such as FFT3D) will work is one of the most challenging problems faced by scientists. The significant effort devoted to this problem has resulted in the appearance of numerous works on performance modeling.The results produced by an exhaustive performance modeling study may predict the performance of a program with a reasonably good accuracy. However, those studies may become unusable because their aim for accuracy can make them so difficult and cumbersome to use that direct experimentation with the program may be preferable, defeating their original purpose. We propose an alternative approach in this paper that does not require a full, accurate, performance model. Our approach mitigates the problem of existing performance models, each one of the parameters and constants in the model has to be carefully measured and tuned, a process that is intrinsically harder than direct experimentation with the program at hand.Instead, we were able to simplify our approach by (1) building performance models that target particular applications in their normal operating conditions and (2) using simpler models that still produce good approximations for the particular case of a program’s normal operating environment.We have conducted experiments using the Blue Fire Supercomputer at the National Center for Atmospheric Research (NCAR), showing that our simplified model can predict the performance of a particular implementation with a high degree of accuracy and very little effort when the program is used in its intended operating range.Finally, although our performance model does not cover extreme cases, we show that its simple approximation under the normal operating conditions of FFT3D is able to provide solid, useful approximations.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01551323/document
%2 https://inria.hal.science/hal-01551323/file/978-3-642-35606-3_23_Chapter.pdf
%L hal-01551323
%U https://inria.hal.science/hal-01551323
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-7513