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In computing, CUDA is a proprietary [2] ... Project Coriander: Converts CUDA C++11 source to OpenCL 1.2 C. A fork of CUDA-on-CL intended to run TensorFlow.
JAX is a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).
CUDA; GeForce 8100 mGPU [44] 2008 MCP78 TSMC 80 nm Unknown Unknown PCIe 2.0 x16 500 1200 400 (system memory) 8:8:4 2 4 Up to 512 from system memory 6.4 12.8 DDR2 64 128 28.8 10.0 3.3 n/a n/a Unknown The block of decoding of HD-video PureVideo HD is disconnected GeForce 8200 mGPU [44] Unknown Unknown gt Unknown PureVideo 3 with VP3
The Tensor Cores use CUDA Warp-Level Primitives on 32 parallel threads to take advantage of their parallel architecture. [39] A Warp is a set of 32 threads which are configured to execute the same instruction. Since Windows 10 version 1903, Microsoft Windows provided DirectML as one part of DirectX to support Tensor Cores.
The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.
As parallel and distributed computing grow in importance in the late 2010s, more investments are being made into efficient high-level APIs for parallel and distributed computing systems such as CUDA, TensorFlow, Hadoop, OpenMP and MPI.