How to Install and Uninstall mmmulti Package on Kali Linux

Last updated: May 19,2024

1. Install "mmmulti" package

This is a short guide on how to install mmmulti on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                mmmulti
            
                    
    
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2. Uninstall "mmmulti" package

Please follow the instructions below to uninstall mmmulti on Kali Linux:


    
        
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                sudo apt remove
            
                        
                mmmulti
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the mmmulti package on Kali Linux

Package: mmmulti
Source: libmmmulti
Version: 0.1-3
Installed-Size: 652
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: libatomic1 (>= 4.8), libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libsdsl3 (>= 2.1.1+dfsg), libstdc++6 (>= 13.1)
Size: 189184
SHA256: 3bdc052f39b03587940000cc340f6545708256dbdcce3b98e32b1286ad986b0d
SHA1: 6bc9d4592d57efd92129b04117235303fe5c63d3
MD5sum: 7b088692386113ce89b86226326d07cd
Description: memory backed multimap
This implements a memory backed multimap intended for use where:
* your keys are integers, or can be mapped to dense range of integers,
* the memory mapped file is on fast storage, like an SSD (although
this is not a requirement),
* you have arbitrary values of fixed size (e.g. structs, other POD
types) that can be sorted,
* you don't need dynamic updates of the table,
* and you are likely to run out of memory of you use a traditional
map or hash table,
* but you can handle approximately 1 bit per record in RAM.
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These may seem to be very specific, but many problems can be mapped into
a dense integer set. mmmulti::map developed first as a data structure
to support seqwish, which uses it to generate precise variation graphs
from pairwise alignments between collections of sequences. As this
multimap forms a key data processing kernel in the algorithm, it can
scale to extremely large problem sizes, limited only by available disk
space. Although performance is much slower than an in-memory structure,
it is virtually guaranteed to be able to complete the compute.
Description-md5:
Multi-Arch: foreign
Homepage: https://github.com/ekg/mmmulti
Section: science
Priority: optional
Filename: pool/main/libm/libmmmulti/mmmulti_0.1-3_amd64.deb