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 6.0.37:
  • Introduced support for the Maxwell architecture (sm_50). More information on Maxwell can be found here: https://developer.nvidia.com/maxwell-compute- architecture. Although the CUDA Toolkit supports developing applications targeted to sm_50, the driver bundled with the CUDA installer does not. Users will need to obtain a driver compatible with the Maxwell architecture from http:// www.nvidia.com/drivers.
  • Unified Memory is a new feature enabling a type of memory that can be accessed by both the CPU and GPU without explicit copying between the two. This is called "managed memory" in the software APIs. Unified Memory is automatically migrated to the physical memory attached to the processor that is accessing it. This migration provides high performance access from either processor, unlike "zero- copy" memory where all accesses are out of CPU system memory.
  • Added a standalone header library for calculating occupancy (the library is not dependent on the CUDA Runtime or CUDA Driver APIs). The header library provides a programmatic interface for the occupancy calculations previously contained in the CUDA Occupancy Calculator. This library is currently in beta status. The interface and implementation are subject to change.
  • The Dynamic Parallelism runtime should no longer generate a cudaErrorLaunchPendingCountExceeded error when the number of
  • pending launches exceeds cudaLimitDevRuntimePendingLaunchCount. Instead, the runtime automatically extends the pending launch buffer beyond cudaLimitDevRuntimePendingLaunchCount, albeit with a performance penalty.
  • Support for the following Linux distributions has been added as of CUDA 6.0: Fedora 19, Ubuntu 13.04, CentOS 5.5+, CentOS 6.4, OpenSUSE 12.3, SLES SP11, and NVIDIA Linux For Tegra (L4T) 19.1.
  • Support for the ICC Compiler has been upgraded to version 13.1.
  • Support for the Windows Server 2012 R2 operating system has been added as of
  • CUDA 6.0.
  • RDMA (remote direct memory access) for GPUDirect is now supported for applications running under MPS (Multi-Process Service).
  • CUDA Inter-Process Communication (IPC) is now supported for applications running under MPS. CUDA IPC event and memory handles can be exported and opened by the MPS clients of a single MPS server.
  • Applications running under MPS can now use assert() in their kernels. When an assert is triggered, all work submitted by MPS clients will be stalled until the assert is handled. The MPS client that triggered the assert will exit, but will not interfere with other running MPS clients.
  • Previously, a wide variety of errors were reported by an "Unspecified
  • Launch Failure (ULF)" message or by the corresponding error codes CUDA_ERROR_LAUNCH_FAILED and cudaErrorLaunchFailed. The CUDA driver now supports enhanced error reporting by providing richer error messages when exceptions occur. This will help developers determine the causes of application faults without the need of additional tools.
Version 6.0.37:
  • Introduced support for the Maxwell architecture (sm_50). More information on Maxwell can be found here: https://developer.nvidia.com/maxwell-compute- architecture. Although the CUDA Toolkit supports developing applications targeted to sm_50, the driver bundled with the CUDA installer does not. Users will need to obtain a driver compatible with the Maxwell architecture from http:// more...
Requirements
Intel, OS X 10.8 or later





MacUpdate - NVIDIA CUDA



NVIDIA CUDA User Discussion (Write a Review)
ver. 6.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

+82
Turquoise commented on 27 Sep 2013
Latest is 5.5.25.
[Version 5.0.36]


burypromote

+368
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

+90

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:6,697
Version Downloads:150
Type:Development : Libraries
License:Free
Date:17 Apr 2014
Platform:Intel 64 / 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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