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Fabien Cadet

STXXL : Standard Template Library for Extra Large Data Sets - 4 views

stxxl.sourceforge.net

C++ STL parallel programming data structures algorithms

shared by Fabien Cadet on 02 Mar 12 - Cached
berbersunny liked it
  • The key features of STXXL are:
  • Transparent support of parallel disks. The library provides implementations of basic parallel disk algorithms. STXXL is the only external memory algorithm library supporting parallel disks.
  • The library is able to handle problems of very large size (tested to up to dozens of terabytes).
  • ...4 more annotations...
  • Improved utilization of computer resources. STXXL implementations of external memory algorithms and data structures benefit from overlapping of I/O and computation.
  • Small constant factors in I/O volume. A unique library feature called "pipelining" can save more than half the number of I/Os, by streaming data between algorithmic components, instead of temporarily storing them on disk. A development branch supports asynchronous execution of the algorithmic components, enabling high-level task parallelism.
  • Shorter development times due to well known STL-compatible interfaces for external memory algorithms and data structures.
  • For internal computation, parallel algorithms from the MCSTL or the libstdc++ parallel mode are optionally utilized, making the algorithms inherently benefit from multi-core parallelism.
  • Fabien Cadet on 02 Mar 12
     
    « The core of STXXL is an implementation of the C++ standard template library STL for external memory (out-of-core) computations, i. e., STXXL implements containers and algorithms that can process huge volumes of data that only fit on disks. »
Andrey Karpov

The Archive of Interesting Code - 0 views

www.keithschwarz.com/interesting

code

shared by Andrey Karpov on 13 Jun 13 - No Cached
  • Andrey Karpov on 13 Jun 13
     
    The Archive of Interesting Code is an (ambitious) effort on my part to research, intuit, and code up every interesting algorithm and data structure ever invented. In doing so, I hope both to learn the mathematical techniques that power these technologies and to improve my skills as a programmer. The examples on this site are in a variety of languages. I generally prefer to use C++ for algorithms, since the STL provides a great framework for expressing algorithms that work on a variety of data types. I code up most data structures in Java, both because the Collections framework allows them to be integrated in seamlessly with other applications and because automatic garbage collection simplifies some of the resource management. Every now and then I'll find an algorithm or data structure that is best represented in a different language like Haskell, in which case I'll forgo my usual language conventions.
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