Memory copies have a suffix that describes the type of a memory transfer, e.g. This is either "memcpy*" for memory copies or the name of a GPU kernel.
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Download NVIDIA CUDA Toolkit for PC today! To get started, browse through online getting started resources, optimization guides, illustrative examples, and collaborate with the rapidly growing developer community. Develop applications using a programming language you already know, including C, C++, Fortran, and Python.
IDE with graphical and command-line tools for debugging, identifying performance bottlenecks on the GPU and CPU, and providing context-sensitive optimization guidance. Using built-in capabilities for distributing computations across multi-GPU configurations, scientists and researchers can develop applications that scale from single GPU workstations to cloud installations with thousands of GPUs. Your CUDA applications can be deployed across all NVIDIA GPU families available on-premise and on GPU instances in the cloud.
For developing custom algorithms, you can use available integrations with commonly used languages and numerical packages as well as well-published development APIs. GPU-accelerated CUDA libraries enable drop-in acceleration across multiple domains such as linear algebra, image and video processing, deep learning, and graph analytics. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to deploy your application. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and HPC supercomputers. NVIDIA CUDA Toolkit provides a development environment for creating high-performance GPU-accelerated applications.
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