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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.
AMDgpu is an open source device driver for the Linux operating system developed by AMD to support its Radeon lineup of graphics cards (GPUs). It was announced in 2014 as the successor to the previous radeon device driver as part of AMD's new "unified" driver strategy, [3] and was released on April 20, 2015.
TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed] Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
Starting with Linux kernel 4.2 AMD Catalyst and Mesa will share the same Linux kernel driver: amdgpu. Amdgpu provides interfaces defined by DRM and KMS. The available free and open-source device drivers for graphic chipsets are "stewarded" by Mesa (because the existing free and open-source implementation of APIs are developed inside of Mesa).
General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).
The Direct Rendering Manager (DRM) is a subsystem of the Linux kernel responsible for interfacing with GPUs of modern video cards.DRM exposes an API that user-space programs can use to send commands and data to the GPU and perform operations such as configuring the mode setting of the display.
Drivers without freely (and legally) -available source code are commonly known as binary drivers. Binary drivers used in the context of operating systems that are prone to ongoing development and change (such as Linux) create problems for end users and package maintainers. These problems, which affect system stability, security and performance ...
CUDA 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. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.