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Spiral

  • Reference work entry
Encyclopedia of Parallel Computing

Definition

Spiral is a program generation system (software that generates other softwares) for linear transforms and an increasing list of other mathematical functions. The goal of Spiral is to automate the development and porting of performance libraries. Linear transforms include the discrete Fourier transform (DFT), discrete cosine transforms, convolution, and the discrete wavelet transform. The input to Spiral consists of a high-level mathematical algorithm specification and selected architectural and microarchitectural parameters. The output is performance-optimized code in a high-level language such as C, possibly augmented with vector intrinsics and threading instructions.

Discussion

Introduction

The advent of computers with multiple cores, SIMD (single-instruction multiple-data) vector instruction sets, and deep memory hierarchies has a dramatic effect on the development of high-performance software. The problem is particularly apparent for functions that perform mathematical...

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Bibliography

  1. Spiral project website.http://www.spiral.net

  2. Bonelli A, Franchetti F, Lorenz J, Püschel M, Ueberhuber CW (2006) Automatic performance optimization of the discrete Fourier transform on distributed memory computers. In: International symposium on parallel and distributed processing and application (ISPA), Lecture notes in computer science, vol 4330. Springer, Berlin, pp 818–832

    Google Scholar 

  3. Chellappa S, Franchetti F, Püschel M (2009) High performance linear transform program generation for the Cell BE. In: Proceedings of the high performance embedded computing (HPEC), Lexington, 22–23 September 2009

    Google Scholar 

  4. de Mesmay F, Chellappa S, Franchetti F, Püschel M (2010) Computer generation of efficient software Viterbi decoders. In: International conference on high performance embedded architectures and compilers (HiPEAC), Lecture notes in computer science, vol 5952. Springer, Berlin, pp 353–368

    Google Scholar 

  5. de Mesmay F, Rimmel A, Voronenko Y, Püschel M (2009) Bandit-based optimization on graphs with application to library performance tuning. In: International conference on machine learning (ICML), ACM international conference proceedings series, vol 382. ACM, New York, pp 729–736

    Google Scholar 

  6. de Mesmay F, Voronenko Y, Püschel M (2010) Offline library adaptation using automatically generated heuristics. In: International parallel and distributed processing symposium (IPDPS)

    Google Scholar 

  7. Franchetti F, de Mesmay F, McFarlin D, Püschel M (2009) Operator language: a program generation framework for fast kernels. In: IFIP working conference on domain specific languages (DSL WC), Lecture notes in computer science, vol 5658. Springer, Berlin, pp 385–410

    Google Scholar 

  8. Franchetti F, Püschel M (2002) A SIMD vectorizing compiler for digital signal processing algorithms. In: International parallel and distributed processing symposium (IPDPS). pp 20–26

    Google Scholar 

  9. Franchetti F, Püschel M (2008) Generating SIMD vectorized permutations. In: International conference on compiler construction (CC), Lecture notes in computer science, vol 4959. Springer, Berlin, pp 116–131

    Google Scholar 

  10. Franchetti F, Püschel M, Voronenko Y, Chellappa S, Moura JMF (2009) Discrete Fourier transform on multicore. IEEE Signal Proc Mag 26(6):90–102

    Google Scholar 

  11. Franchetti F, Voronenko Y, Püschel M (2005) Formal loop merging for signal transforms. In: Programming languages design and implementation (PLDI). ACM, New York, pp 315–326

    Google Scholar 

  12. Franchetti F, Voronenko Y, Püschel M (2006) FFT program generation for shared memory: SMP and multicore. In: Supercomputing (SC). ACM, New York

    Google Scholar 

  13. Franchetti F, Voronenko Y, Püschel M (2006) A rewriting system for the vectorization of signal transforms. In: High performance computing for computational science (VECPAR), Lecture notes in computer science, vol 4395. Springer, Berlin, pp 363–377

    Google Scholar 

  14. Frigo M (1999) A fast Fourier transform compiler. In: Proceedings of the programming language design and implementation (PLDI). ACM, New York, pp 169–180

    Google Scholar 

  15. Frigo M, Johnson SG (2005) The design and implementation of FFTW3. Proc IEEE 93(2):216–231

    Google Scholar 

  16. Johnson J, Johnson RW, Rodriguez D, Tolimieri R (1990) A methodology for designing, modifying, and implementing Fourier transform algorithms on various architectures. IEEE Trans Circ Sys 9:449–500

    MATH  MathSciNet  Google Scholar 

  17. McFarlin D, Franchetti F, Moura JMF, Püschel M (2009) High performance synthetic aperture radar image formation on commodity architectures. Proc SPIE 7337:733708

    Google Scholar 

  18. Milder PA, Franchetti F, Hoe JC, Püschel M (2008) Formal datapath representation and manipulation for implementing DSP transforms. In: Design automation conference (DAC). ACM, New York, pp 385–390

    Google Scholar 

  19. Nordin G, Milder PA, Hoe JC, Püschel M (2005) Automatic generation of customized discrete Fourier transform IPs. In: Design automation conference (DAC). ACM, New York, pp 471–474

    Google Scholar 

  20. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  21. Püschel M, Moura JMF (2008) Algebraic signal processing theory: Cooley-Tukey type algorithms for DCTs and DSTs. IEEE Trans Signal Proces 56(4):1502–1521

    Google Scholar 

  22. Püschel M, Moura JMF, Johnson J, Padua D, Veloso M, Singer B, Xiong J, Franchetti F, Gacic A, Voronenko Y, Chen K, Johnson RW, Rizzolo N (2005) SPIRAL: code generation for DSP transforms. Proc IEEE (Special Issue on Program Generation, Optimization, and Adaptation) 93(2):232–275

    Google Scholar 

  23. Püschel M, Singer B, Veloso M, Moura JMF (2001) Fast automatic generation of DSP algorithms. In: International conference on computational science (ICCS), Lecture notes in computer science, vol 2073. Springer, Berlin, pp 97–106

    Google Scholar 

  24. Püschel M, Singer B, Xiong J, Moura JMF, Johnson J, Padua D, Veloso M, Johnson RW (2004) SPIRAL: a generator for platform-adapted libraries of signal processing algorithms. J High Perform Comput Appl 18(1):21–45

    Google Scholar 

  25. Singer B, Veloso M (2001) Learning to generate fast signal processing implementations. In: International conference on machine learning (ICML). Morgan Kaufmann, San Francisco, pp 529–536

    Google Scholar 

  26. Singer B, Veloso M (2001) Stochastic search for signal processing algorithm optimization. In: Supercomputing (SC). ACM, New York, p 22

    Google Scholar 

  27. Tolimieri R, An M, Lu C (1997) Algorithms for discrete Fourier transforms and convolution, 2nd edn. Springer, Berlin

    Google Scholar 

  28. Van Loan C (1992) Computational framework of the fast Fourier transform. SIAM, Philadelphia

    Google Scholar 

  29. Voronenko Y (2008) Library generation for linear transforms. Ph.D. thesis, Electrical and Computer Engineering, Carnegie Mellon University

    Google Scholar 

  30. Voronenko Y, de Mesmay F, Püschel M (2009) Computer generation of general size linear transform libraries. In: International symposium on code generation and optimization (CGO). IEEE Computer Society, Washington, DC, pp 102–113

    Google Scholar 

  31. Voronenko Y, P\(\ddot{\mathrm{u}}\) schel M (2009) Algebraic signal processing theory: Cooley–Tukey type algorithms for real DFTs. IEEE Trans Signal Proces 57(1):205–222

    Google Scholar 

  32. Xiong J, Johnson J, Johnson RW, Padua D (2001) SPL: a language and compiler for DSP algorithms. In: Programming languages design and implementation (PLDI). ACM, New York, pp 298–308

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, ETH Zurich, Zurich, Switzerland

    Markus Püschel

  2. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Franz Franchetti

  3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Yevgen Voronenko

Authors
  1. Markus Püschel
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  2. Franz Franchetti
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  3. Yevgen Voronenko
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Editor information

Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    David Padua

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Püschel, M., Franchetti, F., Voronenko, Y. (2011). Spiral. In: Padua, D. (eds) Encyclopedia of Parallel Computing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09766-4_244

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