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While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18] TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed]
The binary format is: 1 sign bit; 8 exponent bits; 10 fraction bits (also called mantissa, or precision bits) The total 19 bits fits within a double word (32 bits), and while it lacks precision compared with a normal 32 bit IEEE 754 floating point number, provides much faster computation, up to 8 times on a A100 (compared to a V100 using FP32).
Cards from such vendors differ on implementing data-format support, such as integer and floating-point formats (32-bit and 64-bit). Microsoft introduced a Shader Model standard, to help rank the various features of graphic cards into a simple Shader Model version number (1.0, 2.0, 3.0, etc.).
Compared to a graphics processing unit, TPUs are designed for a high volume of low precision computation (e.g. as little as 8-bit precision) [3] with more input/output operations per joule, without hardware for rasterisation/texture mapping. [4]
In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
On August 12, 2008, AMD released the ATI Radeon HD 4870X2 graphics card with two Radeon R770 GPUs totaling 2.4 teraFLOPS. In November 2008, an upgrade to the Cray Jaguar supercomputer at the Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) raised the system's computing power to a peak 1.64 petaFLOPS, making Jaguar the world's ...
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.
AMD64 (also variously referred to by AMD in their literature and documentation as “AMD 64-bit Technology” and “AMD x86-64 Architecture”) was created as an alternative to the radically different IA-64 architecture designed by Intel and Hewlett-Packard, which was backward-incompatible with IA-32, the 32-bit version of the x86 architecture.