رایانش علمی: تفاوت میان نسخه‌ها

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(۵ نسخهٔ میانی ویرایش شده توسط ۲ کاربر نشان داده نشد)
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== ابزارهای متن باز ==
در اینجا لیستی از ابزارهای متن باز مرتبط با رایانش عملی ارایه شده است:
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* PETSc: A suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations.
* Trilinos Project: A set of robust algorithms and technologies for solving both linear and non-linear equations, developed from work at Sandia National Labs.
* uBLAS: “A C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices and many numerical algorithms for linear algebra.”
* GMP: A free library for arbitrary precision arithmetic, operating on signed integers, rational numbers, and floating-point numbers.
* UMFPACK: A set of routines for solving unsymmetric sparse linear systems, Ax=b, using the Unsymmetric MultiFrontal method.
* ParMETIS: An MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs, meshes, and for computing fill-reducing orderings of sparse matrices.
* NumPy: The fundamental package for scientific computing with Python.
* CGAL: Efficient and reliable geometric algorithms in the form of a C++ library.
* SCOTCH: A software package and libraries for sequential and parallel graph partitioning, static mapping and clustering, sequential mesh and hypergraph partitioning, and sequential and parallel sparse matrix block ordering.
* MPI: A standardized and portable message-passing system designed by a group of researchers from academia and industry to function on a wide variety of parallel computers.
* VTK: An open-source, freely available software system for 3D computer graphics, image processing and visualization.
* SLEPc: A software library for the solution of large scale sparse eigenvalue problems on parallel computers.
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== مثال ==


== لینک های مرتبط ==
== لینک های مرتبط ==
* [http://www.toptal.com/scientific-computing/scientific-computing-with-open-source-tools]
* [[پایتون (زبان برنامه نویسی)]]
* [http://www.toptal.com/scientific-computing/scientific-computing-with-open-source-tools Scientific Computing with Open Source Tools]

نسخهٔ کنونی تا ‏۱۹ سپتامبر ۲۰۱۴، ساعت ۱۰:۳۸

این صفحه در حال تکمیل است. شما نیز میتوانید مشارکت داشته باشید.

ابزارهای متن باز

در اینجا لیستی از ابزارهای متن باز مرتبط با رایانش عملی ارایه شده است:


  • PETSc: A suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations.
  • Trilinos Project: A set of robust algorithms and technologies for solving both linear and non-linear equations, developed from work at Sandia National Labs.
  • uBLAS: “A C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices and many numerical algorithms for linear algebra.”
  • GMP: A free library for arbitrary precision arithmetic, operating on signed integers, rational numbers, and floating-point numbers.
  • UMFPACK: A set of routines for solving unsymmetric sparse linear systems, Ax=b, using the Unsymmetric MultiFrontal method.
  • ParMETIS: An MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs, meshes, and for computing fill-reducing orderings of sparse matrices.
  • NumPy: The fundamental package for scientific computing with Python.
  • CGAL: Efficient and reliable geometric algorithms in the form of a C++ library.
  • SCOTCH: A software package and libraries for sequential and parallel graph partitioning, static mapping and clustering, sequential mesh and hypergraph partitioning, and sequential and parallel sparse matrix block ordering.
  • MPI: A standardized and portable message-passing system designed by a group of researchers from academia and industry to function on a wide variety of parallel computers.
  • VTK: An open-source, freely available software system for 3D computer graphics, image processing and visualization.
  • SLEPc: A software library for the solution of large scale sparse eigenvalue problems on parallel computers.

مثال

لینک های مرتبط