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A Survey of Communication Performance Models for High-Performance Computing

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TLDR
In this article, the authors present the state-of-the-art in analytic communication performance models, providing sufficiently detailed descriptions of particularly noteworthy efforts, as well as future directions for future research in the area of analytical communication performance modeling.
Abstract
This survey aims to present the state of the art in analytic communication performance models, providing sufficiently detailed descriptions of particularly noteworthy efforts. Modeling the cost of communications in computer clusters is an important and challenging problem. It provides insights into the design of the communication pattern of parallel scientific applications and mathematical kernels and sets a clear ground for optimization of their deployment in the increasingly complex high-performance computing infrastructure. The survey provides background information on how different performance models represent the underlying platform and shows the evolution of these models over time from early clusters of single-core processors to present-day multi-core and heterogeneous platforms. Prospective directions for future research in the area of analytic communication performance modeling conclude the survey.

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Citations
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References
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Optimization of Collective Communication Operations in MPICH

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SUMMA: Scalable Universal Matrix Multiplication Algorithm

TL;DR: This paper gives a straight forward, highly efficient, scalable implementation of common matrix multiplication operations that are much simpler than previously published methods, yield better performance, and require less work space.
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