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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.
[45] [46] The USB, PCI-e, and M.2 products function as add-ons to existing computer systems, and support Debian-based Linux systems on x86-64 and ARM64 hosts (including Raspberry Pi). The machine learning runtime used to execute models on the Edge TPU is based on TensorFlow Lite. [47]
Rockchip announced the first member of the RK33xx family at the CES show in January 2015. The RK3368 is a SoC targeting tablets and media boxes featuring a 64-bit octa-core Cortex-A53 CPU and an OpenGL ES 3.1-class GPU. [40] Octa-Core Cortex-A53 64-bit CPU, up to 1.5 GHz; PowerVR SGX6110 GPU with support for OpenGL 3.1 and OpenGL ES 3.0; 28 nm ...
The latest releases also have packages that allow for easy setup for TensorFlow and CUDA. [5] [6] Pop!_OS is maintained primarily by System76, with the release version source code hosted in a GitHub repository. Unlike many other Linux distributions, it is not community-driven, although outside programmers can contribute, view and modify the ...
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.
Alea GPU also provides a simplified GPU programming model based on GPU parallel-for and parallel aggregate using delegates and automatic memory management. [22] MATLAB supports GPGPU acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server, [23] and third-party packages like Jacket.
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.
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).