Abstract
Sparse matrix computations arise in many scientific and engineering applications, but their performance is limited by the growing gap between processor and memory speed. In this paper, we present a case study of an important sparse matrix triple product problem that commonly arises in primal-dual optimization method.
Instead of a generic two-phase algorithm, we devise and implement a single pass algorithm that exploits the block diagonal structure of the matrix. Our algorithm uses fewer floating point operations and roughly half the memory of the two-phase algorithm. The speed-up of the one-phase scheme over the two-phase scheme is 2.04 on a 900 MHz Intel Itanium-2, 1.63 on an 1 GHz Power-4, and 1.99 on a 900 MHz Sun Ultra-3.
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Im, EJ., Bustany, I., Ashcraft, C., Demmel, J.W., Yelick, K.A. (2006). Performance Tuning of Matrix Triple Products Based on Matrix Structure. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958_89
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DOI: https://doi.org/10.1007/11558958_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29067-4
Online ISBN: 978-3-540-33498-9
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