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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.
PyOpenCL, [122] Python interface to OpenCL API Project Coriander: Conversion CUDA to OpenCL 1.2 with CUDA-on-CL [ 123 ] [ 124 ] Lightweight Java Game Library (LWJGL) contains low-lag Java bindings for OpenCL
There is open-source software built on top of the closed-source CUDA, for instance RAPIDS. CUDA is able run on consumer GPUs, whereas ROCm support is mostly offered for professional hardware such as AMD Instinct and AMD Radeon Pro. Nvidia provides a C/C++-centered frontend and its Parallel Thread Execution (PTX) LLVM GPU backend as the Nvidia ...
CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, [18] which supersedes the beta released February 14, 2008. [19]
Milvus joined Linux Foundation as an incubation project in January 2020 and became a graduate in June 2021. [2] The details about its architecture and possible applications were presented on ACM SIGMOD Conference in 2021 [4] Milvus 2.0, a major redesign of the whole product with a new architecture, [5] was released in January 2022.
Simulation Open Framework Architecture (SOFA) [1] is an open source framework primarily targeted at real-time physical simulation, with an emphasis on medical simulation. It is mostly intended for the research community to help develop newer algorithms, but can also be used as an efficient prototyping tool or as a physics engine .
Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]
OpenMM has a C++ API as well as a Python wrapper.Developers are able to customize force fields as well as integrators for low-level simulation control. Users who only require high-level control of their simulations can use built-in force fields (consisting of many commonly used force fields) and built in integrators like Langevin, Verlet, Nosé–Hoover, and Brownian.