Search results
Results from the WOW.Com Content Network
Users do not need to log in or to have a Cloud account, to search for public packages, download and install them. Users can build new Conda packages using Conda-build and then use the Anaconda Client CLI upload packages to Anaconda.org. [53] Notebooks users can be aided with writing and debugging code with Anaconda's AI Assistant. [54]
Conda is an open-source, [2] cross-platform, [3] language-agnostic package manager and environment management system. It was originally developed to solve package management challenges faced by Python data scientists , and today is a popular package manager for Python and R .
The following package management systems distribute the source code of their apps. Either the user must know how to compile the packages, or they come with a script that automates the compilation process. For example, in GoboLinux a recipe file contains information on how to download, unpack, compile and install a package using its Compile tool ...
Open MPI is a Message Passing Interface (MPI) library project combining technologies and resources from several other projects (FT-MPI, LA-MPI, LAM/MPI, and PACX-MPI).It is used by many TOP500 supercomputers including Roadrunner, which was the world's fastest supercomputer from June 2008 to November 2009, [3] and K computer, the fastest supercomputer from June 2011 to June 2012.
Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name. pip has a feature to manage full lists of packages and corresponding version numbers, possible through a "requirements" file. [14]
The Message Passing Interface (MPI) is a portable message-passing standard designed to function on parallel computing architectures. [1] The MPI standard defines the syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C, C++, and Fortran.
It is written in Fortran 90 with parallelism by MPI and it uses BLAS and ScaLAPACK kernels for dense matrix computations. Since 1999, MUMPS has been supported by CERFACS, IRIT-ENSEEIHT, and INRIA. The importance of MUMPS lies in the fact that it is a supported free implementation of the multifrontal method.
ScaLAPACK is designed for heterogeneous computing and is portable on any computer that supports MPI or PVM. ScaLAPACK depends on PBLAS operations in the same way LAPACK depends on BLAS . As of version 2.0, the code base directly includes PBLAS and BLACS and has dropped support for PVM.