NVIDIA and Red Hat have partnered to bring continued improvements to the precompiled NVIDIA Driver introduced in 2020. Last month, NVIDIA announced that the open GPU driver modules will become the default recommended way to enable NVIDIA graphics hardware. Today, NVIDIA announced that Red Hat is now compiling and signing the NVIDIA open GPU kernel modules to further streamline the usage for��
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Historically, the GPU device code is compiled alongside the application with offline tools such as . In this case, the GPU device code is managed internally to the CUDA runtime. You can then launch kernels using and the CUDA runtime ensures that the invoked kernel is launched. However, in some cases, GPU device code needs to be dynamically compiled and loaded. This post shows a way to��
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Python is the most common programming language for data science, machine learning, and numerical computing. It continues to grow in popularity among scientists and researchers. In the Python ecosystem, NumPy is the foundational Python library for performing array-based numerical computations. NumPy��s standard implementation operates on a single CPU core, with only a limited set of operations��
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The latest release of CUDA Toolkit, version 12.4, continues to push accelerated computing performance using the latest NVIDIA GPUs. This post explains the new features and enhancements included in this release: CUDA and the CUDA Toolkit software provide the foundation for all NVIDIA GPU-accelerated computing applications in data science and analytics, machine learning��
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The latest release of CUDA Toolkit continues to push the envelope of accelerated computing performance using the latest NVIDIA GPUs. New features of this release, version 12.3, include: CUDA and the CUDA Toolkit continue to provide the foundation for all accelerated computing applications in data science, machine learning and deep learning, generative AI with LLMs for both training and��
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CUDA is the software development platform for building GPU-accelerated applications, providing all the components you need to develop applications that use NVIDIA GPUs. CUDA is ideal for diverse workloads from high performance computing, data science analytics, and AI applications. The latest release, CUDA 11.3, and its features are focused on enhancing the programming model and performance of��
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CUDA is the software development platform for building GPU-accelerated applications, providing all the components needed to develop applications targeting every NVIDIA GPU platform for general purpose compute acceleration. The latest CUDA release, CUDA 11.2, is focused on improving the user experience and application performance for CUDA developers. CUDA 11.2��
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