In computational complexity theory, computing efficiency, combinatorial optimization, supercomputing, computational cost (algorithmic efficiency) and other computational metrics, the mem is a measurement unit for the number of memory accesses used or needed by a process, function, instruction set, algorithm or data structure. Reducing mems as a speed and efficiency enhancement is not a linear benefit, as it trades off increases in ordinary operations costs.
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| - In computational complexity theory, computing efficiency, combinatorial optimization, supercomputing, computational cost (algorithmic efficiency) and other computational metrics, the mem is a measurement unit for the number of memory accesses used or needed by a process, function, instruction set, algorithm or data structure. Reducing mems as a speed and efficiency enhancement is not a linear benefit, as it trades off increases in ordinary operations costs. (en)
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| - In computational complexity theory, computing efficiency, combinatorial optimization, supercomputing, computational cost (algorithmic efficiency) and other computational metrics, the mem is a measurement unit for the number of memory accesses used or needed by a process, function, instruction set, algorithm or data structure. Example usage, when discussing processing time of a search tree node, for finding 10 × 10 Latin squares: "A typical node of the search tree probably requires about 75 mems (memory accesses) for processing, to check validity. Therefore the total running time on a modern computer would be roughly the time needed to perform 2×1020 mems." (Donald Knuth, 2011, The Art of Computer Programming, Volume 4A, p. 6). Reducing mems as a speed and efficiency enhancement is not a linear benefit, as it trades off increases in ordinary operations costs. (en)
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