Some people confuseCUDA, launched in 2006, for a programming language — or maybe an API. With over 150 CUDA-based libraries, SDKs, and profiling and optimization tools, it represents far more than that. We’re constantly innovating. Thousands of GPU-accelerated applications are built on the ...
by the application to the Cuda device ID and values associated with a particular stream. Applications can use multiple fixed stream contexts or change the values in a particular stream context on the fly whenever a different stream is to be used. Note: NPP 10.2 and beyond contain an additional...
and gaming applications. However, their architecture, characterized by a large number of smaller, efficient cores, makes them exceptionally good at handling parallel tasks. This is a stark contrast to the vast majority of general-purpose CPUs, which are designed for sequential processing. The emergen...
CUDA and OpenCL frameworks allow machine-learning code to use GPUs; GPUs are inefficient for serialized CPU tasks like I/O, preprocessing, post processing, and evaluation metrics. GPU memory might not be enough for larger datasets, requiring multi-GPU setups or CPU+GPU; Cloud GPU-accelerated ...
Applications are software or user interfaces available to use in, or with, an AI Workbench project. For example: JupyterLab — Each of the NVIDIA-provided base container options has JupyterLab installed by default. Visual Studio Code — If VS Code is installed on your local computer, you can...
NVIDIA invented the GPU in 1999. Then with the creation of the NVIDIACUDA® programming model andTesla® GPU platform, NVIDIA brought parallel processing to general-purpose computing. With AI innovation and high-performance computing converging, NVIDIA GPUs powering AI solutions are enabling the wo...
NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing power of GPUs.
To date, the CUDA ecosystem has spawned more than 700 accelerated applications, tackling grand challenges like drug discovery, disaster response and even plans for missions to Mars. Meanwhile, accelerated computing also enabled the next big leap in graphics. In 2018, NVIDIA’s Turing architecture po...
Translates CUDA source code into portable HIP C++ ROCm CMake Collection of CMake modules for common build and development tasks ROCdbgapi ROCm debugger API library ROCm Debugger (ROCgdb) Source-level debugger for Linux, based on the GNU Debugger (GDB) ...
TensorFlow is written both in optimized C++ and the NVIDIA®CUDA®Toolkit, enabling models to run on GPU at training and inference time for massive speedups. TensorFlow GPU support requires several drivers and libraries. To simplify installation and to avoid library conflicts, it’s recommended ...