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Fabien Cadet

STXXL : Standard Template Library for Extra Large Data Sets - 0 views

stxxl.sourceforge.net

C++ STL parallel programming data structures algorithms

shared by Fabien Cadet on 02 Mar 12 - Cached
  • 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. »
Fabien Cadet

[PDF] « Is Parallel Programming Hard, And, If So, What Can You Do About It? »... - 0 views

kernel.org/...perfbook.2011.01.02a.pdf

parallel programming ebook reference

shared by Fabien Cadet on 03 Jan 11 - No Cached
  • Fabien Cadet on 03 Jan 11
     
    « The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity. By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel projects. But you should think of this book as a foundation on which to build, rather than as a completed cathedral. You mission, if you choose to accept, is to help make further progress in the exciting field of parallel programming, progress that should in time render this book obsolete. Parallel programming is not as hard as it is reputed, and it is hoped that this book makes it even easier for you. »
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