NVIDIA CUDA
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Development environment for CUDA-enabled GPUs.   Free
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NVIDIA CUDA is a C language development environment for CUDA-enabled GPUs. The CUDA development environment includes:
  • nvcc C compiler
  • CUDA FFT and BLAS libraries for the GPU
  • Profiler
  • gdb debugger for the GPU (alpha available in March, 2008)
  • CUDA runtime driver (now also available in the standard NVIDIA GPU driver)
  • CUDA programming manual
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
  • Parallel bitonic sort
  • Matrix multiplication
  • Matrix
What's New
Version 5.5.20:

New Features: General CUDA:
  • MPS (Multi-Process Service) is a runtime service designed to let multiple MPI (Message Passing Interface) processes using CUDA run concurrently on a single GPU in a way that's transparent to the MPI program. A CUDA program runs in MPS mode if the MPS control daemon is running on the system. When a CUDA program starts, it connects to the MPS control daemon (if possible), which then creates an MPS server for the connecting client if one does not already exist for the user (UID) that launched the client. See the nvidia-cuda-mps-control man page for more information on how to configure an MPS environment.
  • The CUDA 5.5 Toolkit adds support for Linux on the ARMv7 Architecture. The toolkit comes with a comprehensive set of tools to develop applications for Linux on ARMv7, either natively or cross-platform. Note that only the ARM hard-float floating point ABI is supported.
  • With the CUDA 5.5 Toolkit, there are some restrictions that are now enforced that may cause existing projects that were building on CUDA 5.0 to fail. For projects that use -Xlinker with nvcc, you need to ensure the arguments after -Xlinker are quoted. In CUDA 5.0, -Xlinker -rpath /usr/local/cuda/lib would succeed; in CUDA 5.5 -Xlinker "-rpath /usr/local/cuda/lib" is now necessary.
  • The Toolkit is using a new installer on Windows. The installer is able to install any selection of components and to customize the installation locations per user request.
  • The CUDA Sample projects have makefiles that are now more self-contained and robust. If some dependent libraries are not present on Linux, the top-level makefile does not build them.
  • The CUDA Toolkit and the CUDA Driver are now available for installation as .rpm and .deb installation packages for all the supported Linux distributions, except Ubuntu 10.04 and RHEL 5.5. Those files are accessible on the CUDA Toolkit package repositories. The RPM and Debian package installations support installation of multiple versions. Installations can be updated when a new version of the CUDA Toolkit is available.
  • The following documents are now available in the CUDA toolkit documentation portal:
    • Programming guides: CUDA Video Encoder, CUDA Video Decoder, Developer Guide to Optimus, Parallel Thread Execution (PTX) ISA, Using Inline PTX Assembly in CUDA, NPP Library Programming Guide.
    • Tools manuals: CUDA Binary Utilities.
    • White papers: Floating-Point and IEEE 754 Compliance, Incomplete-LU and Cholesky Preconditioned Iterative Methods.
    • Compiler SDK: libNVVM API, libdevice Users's Guide, NVVM IR Specification.
    • General: CUDA Toolkit Release Notes, End-User License Agreements.
View the complete release notes.
Version 5.5.20:

New Features: General CUDA:
  • MPS (Multi-Process Service) is a runtime service designed to let multiple MPI (Message Passing Interface) processes using CUDA run concurrently on a single GPU in a way that's transparent to the MPI program. A CUDA program runs in MPS mode if the MPS control daemon is running on the system. When a CUDA program starts, it more...
Requirements
Intel, OS X 10.7 or later





MacUpdate - NVIDIA CUDA



NVIDIA CUDA User Discussion (Write a Review)
ver. 5.x:
Your rating: Now say why...
Overall:
(1)

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burypromote

+3
Esquare commented on 16 Apr 2014
Latest now is 6.0.37.
http://www.nvidia.com/object/macosx-cuda-6.0.37-driver.html
[Version 5.5.20]


burypromote

+78
Turquoise commented on 27 Sep 2013
Latest is 5.5.25.
[Version 5.0.36]


burypromote

+372
Nontroppo commented on 18 Jan 2011
While NVidia is constantly moving CUDA forward (kudos to them), and it is found in many pieces of software making real differences to end users (After Effects, Matlab etc.), Apple and Khronos are sat twiddling their thumbs doing nothing with OpenCL. I'm all for open standards, but OpenCL is such a dead duck. Sadly, Apple Mac Pros currently come with ATI cards and thus no software support from major vendors for computing on the GPU...
[Version 3.2]

1 Reply

burypromote

-1
joe_exp replied on 16 Mar 2013
CUDA Driver Version: 5.0.45 available now for 10.8.3 update
burypromote

+91

Beige reviewed on 18 Jan 2011
working great with Genarts and other apps.
[Version 3.2]


burypromote

+206
Mark Everitt commented on 24 Jul 2009
If they do then they risk further alienating the scientific community, which already flirts more than average with GNU/Linux and Mac OS.

Having said that, of course they will.
[Version 2.3.1]


burypromote
+3

+50
Peter da Silva commented on 10 Jun 2009
So... are nVidia and Apple going to collaborate on some merge of CUDA and OpenCL? Or does it matter, since Microsoft will just implement their own incompatible variant in DirectX 13?
[Version 2.2]


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Downloads:6,538
Version Downloads:375
Type:Development : Libraries
License:Free
Date:21 Oct 2013
Platform:Intel 32 / OS X
Price:Free0.00
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NVIDIA CUDA is a C language development environment for CUDA-enabled GPUs. The CUDA development environment includes:
  • nvcc C compiler
  • CUDA FFT and BLAS libraries for the GPU
  • Profiler
  • gdb debugger for the GPU (alpha available in March, 2008)
  • CUDA runtime driver (now also available in the standard NVIDIA GPU driver)
  • CUDA programming manual
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
  • Parallel bitonic sort
  • Matrix multiplication
  • Matrix transpose
  • Performance profiling using timers
  • Parallel prefix sum (scan) of large arrays
  • Image convolution
  • 1D DWT using Haar wavelet
  • Many more features


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