Download Nvidia Cuda Toolkit 5.5.20 For Mac

Freeware
macOS
1.3 GB

Download Nvidia CUDA Toolkit. The CUDA Installers include the CUDA Toolkit, SDK code samples, and developer drivers. CUDA Toolkit: The CUDA Toolkit supplements the CUDA Driver with compilers and additional libraries and header files that are installed into /Developer/NVIDIA/CUDA-10.0 by default. Symlinks are created in /usr/local/cuda/ pointing to their respective files in /Developer/NVIDIA/CUDA- 10.0 /. Nvidia Web Driver and CUDA Installation Instructions for macOS 10.13.4 (17E199) Things To Do 1) install the Nvidia Web Driver 387.10.10.10.30.103. 5/5 Excellent Your rating: not submitted CUDA Toolkit is a collection of powerful tools which will help developers to significantly speed up their GPU fro use in various fields, such as natural resource exploration, medical imaging, and more.

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Features:

  • CUDA Drivers for MAC Archive. AI AND DEEP LEARNING. CUDA ACCELERATED COMPUTING. Download Drivers > CUDA Drivers for MAC Archive. Relevant Links. CUDA Zone; Relevant Links. SIGN UP FOR NVIDIA NEWS. Follow NVIDIA. USA - United States.
  • With NVIDIA CUDA Toolkit, you can freely build GPU-accelerated application software projects. First things first, CUDA is a parallel computing platform and programming model invented by NVIDIA.
  • Download Nvidia CUDA Toolkit. UDA Toolkit is a C language development environment for CUDA-enabled GPUs especially designed for macOS.
  • C/C++ compiler
  • Visual Profiler
  • GPU-accelerated BLAS library
  • GPU-accelerated FFT library
  • GPU-accelerated Sparse Matrix library
  • GPU-accelerated RNG library
  • Additional tools and documentation

Features:

  • Easier Application Porting
  • Share GPUs across multiple threads
  • Use all GPUs in the system concurrently from a single host thread
  • No-copy pinning of system memory, a faster alternative to cudaMallocHost()
  • C++ new/delete and support for virtual functions
  • Support for inline PTX assembly
  • Thrust library of templated performance primitives such as sort, reduce, etc.
  • Nvidia Performance Primitives (NPP) library for image/video processing
  • Layered Textures for working with same size/format textures at larger sizes and higher performance
  • Faster Multi-GPU Programming
  • Unified Virtual Addressing
  • GPUDirect v2.0 support for Peer-to-Peer Communication
  • New & Improved Developer Tools
  • Automated Performance Analysis in Visual Profiler
  • C++ debugging in CUDA-GDB for Linux and MacOS
  • GPU binary disassembler for Fermi architecture (cuobjdump)
  • [Parallel Nsight 2.0](http://developer.nvidia.com/nvidia-parallel-nsight) now available for Windows developers with new debugging and profiling features..

Install Instructions:

Windows

  • Double click cuda_9.0.176_win10_network.exe
  • Follow on-screen prompts

macOS

  • Open cuda_9.0.176_mac_network.dmg
  • Launch the installer
  • Follow the on-screen prompts
Toolkit

Fedora

  • `sudo rpm -i cuda-repo-fedora25-9-0-local-9.0.176-1.x86_64.rpm`
  • `sudo dnf clean all`
  • `sudo dnf install cuda`

OpenSUSE

  • `sudo rpm -i cuda-repo-opensuse422-9-0-local-9.0.176-1.x86_64.rpm`
  • `sudo zypper refresh`
  • `sudo zypper install cuda`

Ubuntu 17.04

  • `sudo dpkg -i cuda-repo-ubuntu1704-9-0-local_9.0.176-1_amd64.deb`
  • `sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub`
  • `sudo apt-get update`
  • `sudo apt-get install cuda`

Ubuntu 16.04

  • `sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb`
  • `sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub`
  • `sudo apt-get update`
  • `sudo apt-get install cuda`

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Freeware
Windows
2.0 GB
39,869

Features:

  • C/C++ compiler
  • Visual Profiler
  • GPU-accelerated BLAS library
  • GPU-accelerated FFT library
  • GPU-accelerated Sparse Matrix library
  • GPU-accelerated RNG library
  • Additional tools and documentation

Nvidia Cuda Toolkit Ubuntu

Highlights:

  • Easier Application Porting
    • Share GPUs across multiple threads
    • Use all GPUs in the system concurrently from a single host thread
    • No-copy pinning of system memory, a faster alternative to cudaMallocHost()
    • C++ new/delete and support for virtual functions
    • Support for inline PTX assembly
    • Thrust library of templated performance primitives such as sort, reduce, etc.
    • Nvidia Performance Primitives (NPP) library for image/video processing
    • Layered Textures for working with same size/format textures at larger sizes and higher performance
  • Faster Multi-GPU Programming
    • Unified Virtual Addressing
    • GPUDirect v2.0 support for Peer-to-Peer Communication
  • New & Improved Developer Tools
    • Automated Performance Analysis in Visual Profiler
    • C++ debugging in CUDA-GDB for Linux and MacOS
    • GPU binary disassembler for Fermi architecture (cuobjdump)
    • Parallel Nsight 2.0 now available for Windows developers with new debugging and profiling features.

What's New:

CUDA 9 is the most powerful software platform for GPU-accelerated applications. It has been built for Volta GPUs and includes faster GPU-accelerated libraries, a new programming model for flexible thread management, and improvements to the compiler and developer tools. With CUDA 9 you can speed up your applications while making them more scalable and robust.

Release Highlights

  • Up to 5X faster libraries with optimizations and heuristics
  • Powerful thread management with cooperative groups
  • Up to 1.5X faster HPC apps with Volta GPUs, NVLINK and HBM2

Libraries

  • Speed up high performance computing (HPC) and deep learning apps with new GEMM kernels in cuBLAS
  • Execute image and signal processing apps faster with performance optimizations across multiple GPU configurations in cuFFT and NVIDIA Performance Primitives
  • Solve linear and graph analytics problems common in HPC with new algorithms in cuSOLVER and nvGRAPH

Cooperative Groups

  • Express rich parallel algorithms with threads from sub-tiles to warps, blocks and grids
  • Manage and reuse threads efficiently within an application with new API and function primitives
  • Replace warp-synchronous programming with robust programming model on Kepler architecture and above

Volta Architecture

  • Execute AI applications faster with Tensor Cores performing 5X faster than Pascal GPUs
  • Scale multi-GPU applications with next generation NVLink delivering 2X throughput of prior generation
  • Increase GPU utilization with Volta Multi-Process Service (MPS)

Development Tools

  • Optimize and pre-fetch memory access by identifying source code causing page faults in unified memory
  • Profile NVLink efficiently by adding events to timeline and color coding connections
  • Inspect unified memory performance bottlenecks with new event filters based on virtual address, migration reason and page fault access type

Complete release notes can be found here.

Download Nvidia Cuda Toolkit 5.5.20 For Mac

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