Virtual memory compression: Difference between revisions

Revision as of 14:44, 5 July 2024

Revision as of 15:06, 5 July 2024

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The first type (1) usually uses some sort of [[LZ77_and_LZ78|LZ]] class dictionary compression algorithm combined with [[entropy coding]], such as [[Lempel–Ziv–Oberhumer|LZO]] or [[LZ4_(compression_algorithm)|LZ4]],<ref name="zswap_kernel_org" /><ref name="zram_kernel_org" /> to compress the pages being swapped out. Once compressed, they are either stored in a swap file in main memory, or written to auxiliary storage, such as a hard disk.<ref name="zswap_kernel_org" /><ref name="zram_kernel_org" /> A two stage process can be used instead wherein there exists both a backing store in auxiliary storage and a swap file in main memory and pages that are evicted from the in-memory swap file are written to the backing store with a greatly decreased bandwidth need due to the compression. This last scheme leverages the benefits of both previous methods : fast in-memory data access with a great increase in the total amount of data that can be swapped out and a decreased bandwidth requirement for data written to auxiliary storage.<ref name="zswap_kernel_org" /><ref name="zram_kernel_org" /><ref name ="CaseForCompressedCaching"/>

One of the most used class of algorithms for the second type (2), the WK class of compression algorithms, takes advantage of in-memory data regularities present in pointers and integers.<ref name ="CaseForCompressedCaching"/><ref name="simpson1SIMPSON" /> Specifically, in (the data segment -- the WK algorithms are not suitable for instruction compression<ref name ="CaseForCompressedCaching"/>) target code generated by most high-level programming languages, both integers and pointers are often present in records whose elements are word-aligned. Furthermore, the values stored in integers are usually small. Also pointers close to each other in memory tend to point to locations that are themselves nearby in memory. Additionally, common data patterns such as a word of all zeroes can be encoded in the compressed output by a very small code (two bits in the case of WKdm). Using these data regularities, the WK class of algorithms use a very small dictionary ( 16 entries in the case of [[WKdm]] ) to achieve up to a 2:1 compression ratio while achieving much greater speeds and having less overhead than LZ class dictionary compression schemes.<ref name ="CaseForCompressedCaching"/><ref name="simpson1SIMPSON" />

==Benefits==

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<ref name="Aul_2015">{{cite web |author-last=Aul |author-first=Gabe |url=http://blogs.windows.com/bloggingwindows/2015/08/18/announcing-windows-10-insider-preview-build-10525/ |title=Announcing Windows 10 Insider Preview Build 10525 |work=Blogging Windows |publisher=[[Microsoft]] |date=2015-08-18 |access-date=2015-08-19}}</ref>

<ref name="Paul_1997_NWDOSTIP">{{cite book |title=NWDOS-TIPs &mdash; Tips & Tricks rund um Novell DOS 7, mit Blick auf undokumentierte Details, Bugs und Workarounds |chapter=Kapitel II.18. Mit STACKER Hauptspeicher 'virtuell' verdoppeln… |language=de |trans-title=NWDOS-TIPs &mdash; Tips & tricks for Novell DOS 7, with a focus on undocumented details, bugs and workarounds |trans-chapter=Utilizing STACKER to 'virtually' double main memory… |author-first=Matthias R. |author-last=Paul |date=1997-07-30 |orig-year=1996-04-14 |edition=3 |version=Release 157 |url=http://www.antonis.de/dos/dos-tuts/mpdostip/html/nwdostip.htm |access-date=2012-01-11 |url-status=live |archive-url=https://web.archive.org/web/20161105172944/http://www.antonis.de/dos/dos-tuts/mpdostip/html/nwdostip.htm |archive-date=2016-11-05}}</ref>

<ref name="simpson1">{{cite journal |last1=Simpson |first1=Matthew |last2=Barua |first2=Rajeev |last3=Biswas |first3=Surupa |date=12 Aug 2023 |title=Analysis of Compression Algorithms for Program Data |url=https://terpconnect.umd.edu/~barua/matt-compress-tr.pdf |journal=University of Maryland |volume= |issue= |pages=4–14 |doi= |access-date=6 Jan 2024}}</ref>

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