Mem (also spelled Meem or Mim) is the thirteenth letter of the Semitic abjads, including Phoenician Mēm , Hebrew Mēm מ, Aramaic Mem , Syriac Mīm ܡܡ, and Arabic Mīm م. Its value is [m].

The Phoenician letter gave rise to the Greek Mu (Μ), Etruscan , Latin M, and Cyrillic М.

Origins

Mem is believed to derive from the Egyptian hieroglyphic symbol for water,

which had been simplified by the Phoenicians and named after their word for water, mem (), ultimately coming from Proto-Semitic *maʾ-/*may-.

Hebrew Mem

Hebrew spelling: מֵם

Hebrew pronunciation

Mem represents a bilabial nasal [m].

Variations on written form/pronunciation

In Hebrew, Mem, like Kaph, Nun, Pe, and Tzadi, has a final form, used at the end of words: its shape changes from מ to ם.

Significance

In gematria, Mem represents the number 40 in both the Standard and Mispar Gadol Methods of Gematria; However, (mem sofit) final mem's value is 40 in the Standard Method and 600 in the Mispar Gadol method. The Standard Method adds the values of Tav and Resh (400+200) to denote the value of mem sofit.

Mem is a given name. Notable people with the name include:

For other meanings, see Mem (disambiguation)

In Computational complexity theory, computing efficiency, Combinatorial optimization, Supercomputing, computational cost (Algorithmic efficiency) and other computational metrics, MEMS 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: "A typical search tree in a (10 x 10 Sudoku or Latin square) requires a node of about 75 mems (memory accesses) for processing, to check validity. Therefore the total running time on a modern processor would be roughly the time needed to perform 7020200000000000000♠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.

PFOR Compression

This optimization technique also is called PForDelta

Although lossless compression methods like Rice, Golomb and PFOR are most often associated with signal processing codecs, the ability to optimize binary integers also adds relevance in reducing MEMS tradeoffs vs. operations. (See Golomb coding for details).