Created by Frank Schluenzen, last modified on Feb 01, 2022 20:51
a library is a collection of non-volatile resources used by computer programs, often for software development. These may include configuration data, documentation, help data, message templates, pre-written code and subroutines, classes, values or type specifications (https://en.wikipedia.org/wiki/Library_(computing)).
List of documented libraries
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alpaka
— Abstraction Library for Parallel Kernel Acceleration: The alpaka library is a header-only C++14 abstraction library for accelerator development. Its aim is to provide performance portability across accelerators through the abstraction (not hiding!) of the underlying levels of parallelism.
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atlas
— The ATLAS (Automatically Tuned Linear Algebra Software) project is an ongoing research effort focusing on applying empirical techniques in order to provide portable performance. At present, it provides C and Fortran77 interfaces to a portably efficient BLAS implementation, as well as a few routines from LAPACK.
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blacs
— The BLACS (Basic Linear Algebra Communication Subprograms) is a package that attempts to provide the same ease of use and portability for distributed memory linear algebra communication that the BLAS provide for linear algebra computation.
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blas
— The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations.
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elpa
— Eigenvalue SoLvers for Petaflop-Applications (ELPA).
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hdf5
— Hierarchical Data Format 5 (HDF5) is a unique open source technology suite for managing data collections of all sizes and complexity.
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llama
— Low-Level Abstraction of Memory Access. LLAMA is a cross-platform C++17 template header-only library for the abstraction of memory access patterns.
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scalapack
— Scalable Linear Algebra PACKage, or Scalable LAPACK, is a software package provided by Univ. of Tennessee; Univ. of California, Berkeley; Univ. of Colorado Denver; and NAG Ltd..
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scotch
— Scotch is a software package for graph and mesh/hypergraph partitioning, graph clustering, and sparse matrix ordering.