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A methodology for generating efficient disk-based algorithms from tensor product formulas

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Languages and Compilers for Parallel Computing (LCPC 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 768))

Abstract

In this paper, we address the issue of automatic generation of disk-based algorithms from tensor product formulas. Disk-based algorithms are required in scientific applications which work with large data sets that do not fit entirely into main memory. Tensor products have been used for designing and implementing block recursive algorithms on shared-memory, vector and distributed-memory multiprocessors. We extend this theory to generate disk-based code from tensor product formulas. The methodology is based on generating algebraically equivalent tensor product formulas which have better disk performance. We demonstrate this methodology by generating disk-based code for the fast Fourier transform.

This work was supported in part by DARPA, order number 7898, monitored by NIST under grant number 60NANB1D1151, DARPA, order number 7899, monitored by NIST under grant number 60NANB1D1150.

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Authors and Affiliations

  1. Department of Computer and Information Science, Ohio State University, USA

    S. D. Kaushik, C. -H. Huang & P. Sadayappan

  2. Department of Computer Science, St. Cloud State University, USA

    R. W. Johnson

Authors
  1. S. D. Kaushik
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  2. C. -H. Huang
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  3. R. W. Johnson
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  4. P. Sadayappan
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Editor information

Utpal Banerjee David Gelernter Alex Nicolau David Padua

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© 1994 Springer-Verlag Berlin Heidelberg

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Kaushik, S.D., Huang, C.H., Johnson, R.W., Sadayappan, P. (1994). A methodology for generating efficient disk-based algorithms from tensor product formulas. In: Banerjee, U., Gelernter, D., Nicolau, A., Padua, D. (eds) Languages and Compilers for Parallel Computing. LCPC 1993. Lecture Notes in Computer Science, vol 768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57659-2_21

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  • DOI: https://doi.org/10.1007/3-540-57659-2_21

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  • Print ISBN: 978-3-540-57659-4

  • Online ISBN: 978-3-540-48308-3

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