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A more recent release is available see the CUDA Toolkit and GPU Computing SDK home page

The CUDA Toolkit is transitioning to a faster release cadence to deliver new features. Big Thanks goes to Barnaclues; Cuda - youv. CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance. The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime. The CUDA container images provide an easy-to-use distribution for CUDA supported platforms and architectures. End User License Agreements.

Carbon copy cloner apps. For older releases, see theCUDA Toolkit Release Archive Adobe cs6 master collection mac trial download.

Release Highlights

• Support for the new Fermi architecture, with:
  • Native 64-bit GPU support
  • Multiple Copy Engine support
  • ECC reporting
  • Concurrent Kernel Execution
  • Fermi HW debugging support in cuda-gdb
  • Fermi HW profiling support for CUDA C and OpenCL in Visual Profiler
• C++ Class Inheritance and Template Inheritance support for increased programmer productivity
• A new unified interoperability API for Direct3D and OpenGL, with support for:
  • OpenGL texture interop
  • Direct3D 11 interop support
• CUDA Driver / Runtime Buffer Interoperability, which allows applications using the CUDA Driver API to also use libraries implemented using the CUDA C Runtime such as CUFFT and CUBLAS.
• CUBLAS now supports all BLAS1, 2, and 3 routines including those for single and double precision complex numbers
• Up to 100x performance improvement while debugging applications with cuda-gdb
• cuda-gdb hardware debugging support for applications that use the CUDA Driver API
• cuda-gdb support for JIT-compiled kernels
• New CUDA Memory Checker reports misalignment and out of bounds errors, available as a stand-alone utility and debugging mode within cuda-gdb
• CUDA Toolkit libraries are now versioned, enabling applications to require a specific version, support multiple versions explicitly, etc.
• CUDA C/C++ kernels are now compiled to standard ELF format
• Support for device emulation mode has been packaged in a separate version of the CUDA C Runtime (CUDART), and is deprecated in this release. Now that more sophisticated hardware debugging tools are available and more are on the way, NVIDIA will be focusing on supporting these tools instead of the legacy device emulation functionality.
  • On Windows, use the new Parallel Nsight development environment for Visual Studio, with integrated GPU debugging and profiling tools (was code-named 'Nexus'). Please seewww.nvidia.com/nsightfor details.
  • On Linux, use cuda-gdb and cuda-memcheck, and check out the solutions from Allinea and TotalView that will be available soon.
• Support for all the OpenCL features in the latest R195 production driver package:
  • Double Precision
  • Graphics Interoperability with OpenCL, Direc3D9, Direct3D10, and Direct3D11 for high performance visualization
  • Query for Compute Capability, so you can target optimizations for GPU architectures (cl_nv_device_attribute_query)
  • Ability to control compiler optimization settings via support for pragma unroll in OpenCL kernels and an extension that allows programmers to set compiler flags. (cl_nv_compiler_options)
  • OpenCL Images support, for better/faster image filtering
  • 32-bit global and local atomics for fast, convenient data manipulation
  • Byte Addressable Stores, for faster video/image processing and compression algorithms
  • Support for the latest OpenCL spec revision 1.0.48 and latest official Khronos OpenCL headers as of 2010-02-17

Note: The developer driver packages below provide baseline support for the widest number of NVIDIA products in the smallest number of installers. More recent production driver packages for developers and end users may be available atwww.nvidia.com/drivers.

For additional tools and solutions for Windows, Linux and MAC OS , such as CUDA Fortran, CULA, CUDA-dgb , please visit our Tools and Ecosystem Page

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Windows XP, Windows VISTA, Windows 7

Linux

MacOS

Nvidia Cuda Toolkit 9

The NVIDIA® CUDA® Toolkit provides a development environment for creating high performance GPU-accelerated applications. 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. The toolkit includes 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 x86, Arm and POWER.

Nvidia Cuda Toolkit 11.0

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.

CUDA 11 Features

Nvidia Cuda Toolkit

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