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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.
In computing, CUDA 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.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the 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 ]
rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface ( API ), it allows the allocation of one or more CUDA-enabled GPUs to a single application.
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).
Hundreds of packages are GPU-accelerated: [123] Nvidia GPUs have support with CUDA.jl (tier 1 on 64-bit Linux and tier 2 on 64-bit Windows, the package implementing PTX, for compute capability 3.5 (Kepler) or higher; both require CUDA 11
6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 4–8 GiB 7–10 W 2023 Jetson Orin NX 70–100 TOPS 1024-core Nvidia Ampere architecture GPU with 32 Tensor cores up to 8-core ARM Cortex-A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3 8–16 GiB 10–25 W 2023 Jetson AGX Orin 200-275 TOPS up to 2048-core Nvidia Ampere architecture GPU with 64 ...