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Abstract: This paper reports on our work and results framing signal processing algorithm optimization as a machine learning task. A single signal processing algorithm can be represented by many different but mathematically equivalent formulas. When these formulas are implemented in actual code, they have very different running times. Signal processing optimization is concerned with finding a formula that implements the algorithm as efficiently as possible. Unfortunately, a correct mapping... (Update)
Context of citations to this paper: More
.... These include FFTW for discrete Fourier transforms [7] ATLAS [18] for the BLAS, Sparsity [9] for sparse matrix vector multiply, and SPIRAL [8, 15] for signal and image processing. Vadhiyar, et al. 16] explore automatically tuning MPI collective operations. These sys 1 The...
.... or during the lifetime of a program [1, 6, 11] O line approaches include architectural tuning systems for BLAS [2, 12] or DSP kernels [9]. For embedded systems an o line approach is best suited since high compilation times can be amortized across the number of systems...
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Statistical Models for Automatic Performance Tuning - Vuduc, Demmel, Bilmes (2001) (Correct)
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On Statistical Models in Automatic Tuning - Vuduc, Demmel, Bilmes (Correct)
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5: Automatically Tuned Linear Algebra Software - Whaley, Dongarra - 1997
5: Optimizing Matrix Multiply using PHiPAC: a Portable - Bilmes, Asanovic et al. - 1996
4: High-Level Optimization via Automated Statistical Modeling (context) - Brewer - 1995
BibTeX entry: (Update)
B. Singer and M. Veloso. Learning to predict performance from formula modeling and training data. In Proc. of the 17th Int'l Conf. on Mach. Learn., 2000. http://citeseer.ist.psu.edu/singer00learning.html More
@inproceedings{ singer00learning,
author = "Bryan Singer and Manuela Veloso",
title = "Learning to Predict Performance from Formula Modeling and Training Data",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "887--894",
year = "2000",
url = "citeseer.ist.psu.edu/singer00learning.html" }
Citations (may not include all citations):157 Automatically tuned linear algebra software - Whaley, Dongarra - 1998
124 FFTW: An adaptive software architecture for the FFT - Frigo, Johnson - 1998
123 Optimizing matrix multiply using PHiPAC: a Portable - Bilmes, Asanovi'c et al. - 1997
108 Discrete cosine transform (context) - Rao, Yip - 1990
55 Algorithms for discrete Fourier transforms and convolution (context) - Tolimieri, An et al. - 1997
37 High-level optimization via automated statistical modeling (context) - Brewer - 1995
21 SPIRAL: Portable Library of Optimized Signal Processing Algo.. (context) - Moura, Johnson et al. - 1998
13 Automatic implementation of FFT algorithms (context) - Auslander, Johnson et al. - 1996
2 Automated formula generation and performance learning for th.. - Singer, Veloso - 2000
Documents on the same site (http://www.ece.cmu.edu/~spiral/publ.html):
Performance Models and Search Methods for Optimal FFT.. - Sepiashvili (2000) (Correct)
An Investigation of Cooley-Tukey Decompositions for the FFT - Haentjens (2000) (Correct)
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