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CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. [15] In 2011, PyPy started development on an implementation of the NumPy API for PyPy. [16] As of 2023, it is not yet fully compatible with NumPy. [17]
General purpose numerical analysis library with C++, C#, Python, FreePascal interfaces. Armadillo [2] [3] NICTA: C++ 2009 12.6.6 / 10.2023 Free Apache License 2.0: C++ template library for linear algebra; includes various decompositions and factorisations; syntax is similar to MATLAB. ATLAS: R. Clint Whaley et al. C 2001 3.10.3 / 07.2016 Free BSD
In shared memory model the processors are all connected to a "globally available" memory, via either software or hardware means. The operating system usually maintains its memory coherence. [4] From a programmer's point of view, this memory model is better understood than the distributed memory model.
More generally, there are d! possible orders for a given array, one for each permutation of dimensions (with row-major and column-order just 2 special cases), although the lists of stride values are not necessarily permutations of each other, e.g., in the 2-by-3 example above, the strides are (3,1) for row-major and (1,2) for column-major.
This is both because of the limitation that most mesh generators are interactive, and because mesh generation runtime is typically insignificant compared to solver time. However, if the mesh is too large to fit in the memory of a single serial machine, or the mesh must be changed (adapted) during the simulation, meshing is done in parallel.
ATLAS is a portable library that automatically optimizes itself for an arbitrary architecture. iMKL is a freeware [7] and proprietary [8] vendor library optimized for x86 and x86-64 with a performance emphasis on Intel processors. [9] OpenBLAS is an open-source library that is hand-optimized for many of the popular architectures.
[8] Distributed data processing can be done in Python using the Python Programmable Filter. This filter functions seamlessly with NumPy and SciPy. Additional modules can be added by either writing an XML description of the interface or by writing C++ classes. The XML interface allows users/developers to add their own VTK filters to ParaView ...