Abstract
This paper introduces a formal framework for automatically generating performance optimized implementations of the discrete Fourier transform (DFT) for distributed memory computers. The framework is implemented as part of the program generation and optimization system Spiral. DFT algorithms are represented as mathematical formulas in Spiral’s internal language SPL. Using a tagging mechanism and formula rewriting, we extend Spiral to automatically generate parallelized formulas. Using the same mechanism, we enable the generation of rescaling DFT algorithms, which redistribute the data in intermediate steps to fewer processors to reduce communication overhead. It is a novel feature of these methods that the redistribution steps are merged with the communication steps of the algorithm to avoid additional communication overhead. Among the possible alternative algorithms, Spiral’s search mechanism now determines the fastest for a given platform, effectively generating adapted code without human intervention. Experiments with DFT MPI programs generated by Spiral show performance gains of up to 30% due to rescaling. Further, our generated programs compare favorably with Fftw-MPI 2.1.5.
This work was supported by the Special Research Program SFB F011 “AURORA” and the Erwin Schrödinger Fellowship of the Austrian Science Fund FWF, and in part by DARPA through the Department of Interior grant NBCH1050009 and by NSF through awards 0234293 and 0325687.
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Bonelli, A., Franchetti, F., Lorenz, J., Püschel, M., Ueberhuber, C.W. (2006). Automatic Performance Optimization of the Discrete Fourier Transform on Distributed Memory Computers. In: Guo, M., Yang, L.T., Di Martino, B., Zima, H.P., Dongarra, J., Tang, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2006. Lecture Notes in Computer Science, vol 4330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946441_74
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DOI: https://doi.org/10.1007/11946441_74
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