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ROCm [3] is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing.
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.
In June 2020, a benchmark with 173 tests on WSL 2 (20H2) with an AMD Ryzen Threadripper 3970X showed an average of 87% of the performance of native Ubuntu 20.04 LTS. In contrast, WSL 1 had only 70% of the performance of native Ubuntu. WSL 2 improves I/O performance, providing a near-native level. [49]
Project Coriander: Converts CUDA C++11 source to OpenCL 1.2 C. A fork of CUDA-on-CL intended to run TensorFlow. [28] [29] [30] CU2CL: Convert CUDA 3.2 C++ to OpenCL C. [31] GPUOpen HIP: A thin abstraction layer on top of CUDA and ROCm intended for AMD and Nvidia GPUs. Has a conversion tool for importing CUDA C++ source.
It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. [5] [6] The primary functions of JAX are: [2] grad: automatic differentiation; jit: compilation; vmap: auto-vectorization; pmap: Single program, multiple data (SPMD) programming
In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. [30] In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be ...
[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]