标签：functions Intel GNU processing library Library CUDA
GNU Octave The Octave syntax is largely compatible with Matlab. The Octave interpreter can be run in GUI mode, as a console, or invoked as part of a shell script.
- Powerful mathematics-oriented syntax with built-in 2D/3D plotting and visualization tools
- Free software, runs on GNU/Linux, macOS, BSD, and Microsoft Windows
- Drop-in compatible with many Matlab scripts
GNU Octave 6.4.0 Released – Oct 30, 2021
The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.
Current stable version: GSL-2.7, released on 1 June 2021.
Intel Integrated Performance Primitives (Intel IPP) is a multi-threaded software library of functions for multimedia and data processing applications, produced by Intel. The library supports Intel and compatible processors and is available for Linux, macOS, Windows and Android operating systems. It is available separately or as a part of Intel Parallel Studio.
The library takes advantage of processor features including MMX, SSE, SSE2, SSE3, SSSE3, SSE4, AVX, AVX2, AVX-512, AES-NI and multi-core processors. Intel IPP includes functions for:
- Video decode/encode
- Audio decode/encode
- JPEG/JPEG2000/JPEG XR
- Computer vision
- Cryptography
- Data compression
- Image color conversion
- Image processing
- Ray tracing and Rendering
- Signal processing
- Speech coding
- Speech recognition
- String processing
- Vector and matrix mathematics
Intel IPP is divided into four major processing groups: Signal (with linear array or vector data), Image (with 2D arrays for typical color spaces), Matrix (with nxm arrays for matrix operations), and Cryptography.
Half the entry points are of the matrix type, a third are of the signal type and the remainder are of the image and cryptography types. Intel IPP functions are divided into 4 data types: Data types include 8u (8-bit unsigned), 8s (8-bit signed), 16s, 32f (32-bit floating-point), 64f, etc. Typically, an application developer works with only one dominant data type for most processing functions, converting between input to processing to output formats at the end points.
History
- Version 2.0 files are dated April 22, 2002.
- Version 2020 Update 2, July 16, 2020
CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia.[1] It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order):
- cuBLAS – CUDA Basic Linear Algebra Subroutines library
- CUDART – CUDA Runtime library
- cuFFT – CUDA Fast Fourier Transform library
- cuRAND – CUDA Random Number Generation library
- cuSOLVER – CUDA based collection of dense and sparse direct solvers
- cuSPARSE – CUDA Sparse Matrix library
- NPP – NVIDIA Performance Primitives library
- nvGRAPH – NVIDIA Graph Analytics library
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标签：functions,Intel,GNU,processing,library,Library,CUDA 来源： https://www.cnblogs.com/funwithwords/p/15894425.html
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