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- NVIDIA has released CUDA 11.3, a new version of its CUDA toolkit and development environment that includes GPU-accelerated libraries and debugging tools.
- The new release includes enhancements to the CUDA programming model, CUDA graph, and Stream-Ordered Memory Allocator, as well as improved language support for CUDAC++ and Python.
- CUDA 11.3 is particularly useful for diverse workloads such as high performance computing, data science analytics, and AI applications, with Python support being a key feature for data science and AI applications.
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NVIDIA CUDA 11.3
NVIDIA has announced our newest release of the CUDA toolkit and development environment, consisting of GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including NVIDIA Ampere, x86, Arm server processors, and POWER.
CUDA 11.3 features are focused on enhancing the programming model and performance of your GPU-accelerated applications.
New features include:
- CUDA programming model enhancements
- CUDA graph enhancements
- Stream-Ordered Memory Allocator enhancements
- Language support for CUDA
- C++ support enhancements
- Python support via preview release on GitHub
- Compiler enhancements
CUDA is ideal for diverse workloads from high performance computing, data science analytics, and AI applications. With that, we are expanding the market opportunity with Python in data science and AI applications.
CUDA Python
CUDA Python is a preview release providing Cython/Python wrappers for CUDA driver and runtime APIs. Python developers will be able to leverage massively parallel GPU computing to achieve faster results and accuracy.
Learn more about CUDA Python.
For more information, read these new posts:
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About Brad Nemire
Brad Nemire leads the Developer Communications team at NVIDIA. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University and currently resides in Silicon Valley.
Brad Nemire leads the Developer Communications team at NVIDIA. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University and currently resides in Silicon Valley.