Virtual memory compression: Difference between revisions

Revision as of 13:20, 1 September 2021

Revision as of 11:10, 3 December 2021

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First, the programmer has to manually implement conversions

and the additional instructions that quantize and dequantize

values, imposing a programmer’sprogrammer's effort and performance overhead. Second, to cover outliers, the bitwidth of the quantized

values often become greater than or equal to the original

values. Third, the programmer has to use standard bitwidth;

otherwise, extracting non-standard bitwidth (i.e., 1-71–7, 9-159–15, and

17-3117–31) for representing narrow integers exacerbates the overhead

of software-based quantization. A hardware support in the memory hierarchy of

general-purpose processors for quantization can solve these problems. Tha hardware support allows representing

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ratio for floating-point values and cache blocks with multiple data

types, and (iii) lower overhead for locating the compressed blocks.<ref name="Quant"/>

==History==

Virtual memory compression has gone in and out of favor as a technology. The price and speed of RAM and external storage have plummeted due to [[Moore's Law]] and improved RAM interfaces such as [[DDR3]], thus reducing the need for virtual memory compression, while multi-core processors, server farms, and mobile technology together with the advent of flash based systems make virtual memory compression more attractive.