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Open AccessArticle

Ideal and Predictable Hit Ratio for Matrix Transposition in Data Caches

Departamento de Informática e Ing. de Sist., I3A, Universidad de Zaragoza, HiPEAC, 50009 Zaragoza, Spain
Departamento de Arquitectura y Tecnología de Computadores, Universidad del País Vasco, HiPEAC, 48940 Leioa, Spain
Author to whom correspondence should be addressed.
Mathematics 2020, 8(2), 184;
Received: 27 December 2019 / Revised: 22 January 2020 / Accepted: 23 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue Supercomputing and Mathematics)
Matrix transposition is a fundamental operation, but it may present a very low and hardly predictable data cache hit ratio for large matrices. Safe (worst-case) hit ratio predictability is required in real-time systems. In this paper, we obtain the relations among the cache parameters that guarantee the ideal (predictable) data hit ratio assuming a Least-Recently-Used (LRU) data cache. Considering our analytical assessments, we compare a tiling matrix transposition to a cache oblivious algorithm, modified with phantom padding to improve its data hit ratio. Our results show that, with an adequate tile size, the tiling version results in an equal or better data hit ratio. We also analyze the energy consumption and execution time of matrix transposition on real hardware with pseudo-LRU (PLRU) caches. Our analytical hit/miss assessment enables the usage of a data cache for matrix transposition in real-time systems, since the number of misses in the worst case is bound. In general and high-performance computation, our analysis enables us to restrict the cache resources devoted to matrix transposition with no negative impact, in order to reduce both the energy consumption and the pollution to other computations.
Keywords: transposition; data cache memory; real-time; tiling; cache oblivious transposition; data cache memory; real-time; tiling; cache oblivious
MDPI and ACS Style

Pedro-Zapater, A.; Rodríguez, C.; Segarra, J.; Gran Tejero, R.; Viñals-Yúfera, V. Ideal and Predictable Hit Ratio for Matrix Transposition in Data Caches. Mathematics 2020, 8, 184.

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