NVIDIA CUDA Getting Started Guide for Mac OS X DU-05348-001v6.5 2 Table 1 Mac Operating System Support in CUDA 6.5 Operating System Native x8664 GCC Clang Mac OS X 10.9.x YES 5.0, 4.2 Mac OS X 10.8.x YES 4.2.1 5.0 Before installing the CUDA Toolkit, you should read the. Nov 29, 2012 Mac OS X Support Mac Software Other Apple Hardware Laptops Laptop Compatibility Catalina Laptop Support Catalina Laptop Guides. Support to CUDA. Please visit Nvidia's official site to know every thing about cuda. Joined Aug 3, 2011 Messages 79 Motherboard Supermicro X8DTi-F CPU Dual Xeon X5650 Graphics Gigabyte GTX 660.
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A more recent release is available see the CUDA Toolkit and GPU Computing SDK home page
For older releases, see theCUDA Toolkit Release Archive
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
Cuda Gpu Emulator
Description of Download | Link to Binaries | Documents |
Developer Drivers for WinXP (197.13) | 32-bit 64-bit | |
Developer Drivers for WinVista & Win7 (197.13) | 32-bit 64-bit | |
Notebook Developer Drivers for WinXP | 32-bit 64-bit | |
Notebook Developer Drivers for WinVista & Win7 | 32-bit 64-bit | |
CUDA Toolkit
| 32-bit 64-bit | Getting Started Guide for Windows Release Notes CUDA C Programming Guide CUDA C Best Best Practices Guide OpenCL Programming Guide OpenCL Best Best Practices Guide OpenCL Implementation Notes CUDA Reference Manual API Reference PTX ISA 2.0 Visual Profiler User Guide Visual Profiler Release Notes Fermi Compatibility Guide Fermi Tuning Guide CUBLAS User Guide CUFFT User Guide License |
NVIDIA Performance Primitives (NPP) library | 32-bit 64-bit | |
GPU Computing SDK code samples | 32-bit 64-bit | Release Notes for CUDA C Release Notes for DirectCompute Release Notes for OpenCL CUDA Occupancy Calculator License |
NVIDIA OpenCL Extensions | Compiler_Options D3D9 Sharing D3D10 Sharing D3D11 Sharing Device Attribute Query Pragma Unroll |
Linux
Description of Download | Link to Binaries | Documents |
Developer Drivers for Linux (195.36.15) | 32-bit 64-bit | |
CUDA Toolkit
| Getting Started Guide for Linux Release Notes for Linux CUDA C Programming Guide CUDA C Best Best Practices Guide OpenCL Programming Guide OpenCL Best Best Practices Guide OpenCL Implementation Notes CUDA Reference Manual API Reference PTX ISA 2.0 CUDA-GDB User Manual Visual Profiler User Guide Visual Profiler Release Notes Fermi Compatibility Guide Fermi Tuning Guide CUBLAS User Guide CUFFT User Guide License | |
CUDA Toolkit for Fedora 10 | 32-bit 64-bit | |
CUDA Toolkit for RedHat Enterprise Linux 5.3 | 32-bit 64-bit | |
CUDA Toolkit for Ubuntu Linux 9.04 | 32-bit 64-bit | |
CUDA Toolkit for RedHat Enterprise Linux 4.8 | 32-bit 64-bit | |
CUDA Toolkit for OpenSUSE 11.1 | 32-bit 64-bit | |
CUDA Toolkit for SUSE Linux Enterprise Desktop 11 | 32-bit 64-bit | |
NVIDIA Performance Primitives (NPP) library | 32-bit 64-bit | |
GPU Computing SDK code samples | download | Release Notes for CUDA C Release Notes for OpenCL CUDA Occupancy Calculator License |
NVIDIA OpenCL Extensions | Compiler_Options D3D9 Sharing D3D10 Sharing D3D11 Sharing Device Attribute Query Pragma Unroll |
MacOS
Description of Download | Link to Binaries | Documents |
Developer Drivers for MacOS | download | |
CUDA Toolkit
| download | Getting Started Guide for Mac Release Notes for Mac CUDA C Programming Guide CUDA C Best Best Practices Guide OpenCL Programming Guide OpenCL Best Best Practices Guide OpenCL Implementation Notes CUDA Reference Manual API Reference PTX ISA 2.0 Visual Profiler User Guide Visual Profiler Release Notes Fermi Compatibility Guide Fermi Tuning Guide CUBLAS User Guide CUFFT User Guide License |
NVIDIA Performance Primitives (NPP) library | download | |
GPU Computing SDK code samples | download | Release Notes for CUDA C Release Notes for OpenCL CUDA Occupancy Calculator License |
CUDA Toolkit Documentation - v11.0.194 (older) - Last updated July 7, 2020 - Send Feedback
- Release Notes
- The Release Notes for the CUDA Toolkit.
- EULA
- The CUDA Toolkit End User License Agreement applies to the NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA Display Driver, NVIDIA Nsight tools (Visual Studio Edition), and the associated documentation on CUDA APIs, programming model and development tools. If you do not agree with the terms and conditions of the license agreement, then do not download or use the software.
Installation Guides
- Quick Start Guide
- This guide provides the minimal first-steps instructions for installation and verifying CUDA on a standard system.
- Installation Guide Windows
- This guide discusses how to install and check for correct operation of the CUDA Development Tools on Microsoft Windows systems.
- Installation Guide Mac OS X
- This guide discusses how to install and check for correct operation of the CUDA Development Tools on Mac OS X systems.
- Installation Guide Linux
- This guide discusses how to install and check for correct operation of the CUDA Development Tools on GNU/Linux systems.
Programming Guides
- Programming Guide
- This guide provides a detailed discussion of the CUDA programming model and programming interface. It then describes the hardware implementation, and provides guidance on how to achieve maximum performance. The appendices include a list of all CUDA-enabled devices, detailed description of all extensions to the C++ language, listings of supported mathematical functions, C++ features supported in host and device code, details on texture fetching, technical specifications of various devices, and concludes by introducing the low-level driver API.
- Best Practices Guide
- This guide presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. The intent is to provide guidelines for obtaining the best performance from NVIDIA GPUs using the CUDA Toolkit.
- Maxwell Compatibility Guide
- This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Maxwell Architecture. This document provides guidance to ensure that your software applications are compatible with Maxwell.
- Pascal Compatibility Guide
- This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Pascal Architecture. This document provides guidance to ensure that your software applications are compatible with Pascal.
- Volta Compatibility Guide
- This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Volta Architecture. This document provides guidance to ensure that your software applications are compatible with Volta.
- Turing Compatibility Guide
- This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Turing Architecture. This document provides guidance to ensure that your software applications are compatible with Turing.
- NVIDIA Ampere GPU Architecture Compatibility Guide
- This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Ampere GPU Architecture. This document provides guidance to ensure that your software applications are compatible with NVIDIA Ampere GPU architecture.
- Kepler Tuning Guide
- Kepler is NVIDIA's 3rd-generation architecture for CUDA compute applications. Applications that follow the best practices for the Fermi architecture should typically see speedups on the Kepler architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Kepler architectural features.
- Maxwell Tuning Guide
- Maxwell is NVIDIA's 4th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Kepler architecture should typically see speedups on the Maxwell architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Maxwell architectural features.
- Pascal Tuning Guide
- Pascal is NVIDIA's 5th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Maxwell architecture should typically see speedups on the Pascal architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Pascal architectural features.
- Volta Tuning Guide
- Volta is NVIDIA's 6th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Pascal architecture should typically see speedups on the Volta architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Volta architectural features.
- Turing Tuning Guide
- Turing is NVIDIA's 7th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Pascal architecture should typically see speedups on the Turing architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Turing architectural features.
- NVIDIA Ampere GPU Architecture Tuning Guide
- NVIDIA Ampere GPU Architecture is NVIDIA's 8th-generation architecture for CUDA compute applications. Applications that follow the best practices for the NVIDIA Volta architecture should typically see speedups on the NVIDIA Ampere GPU Architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging NVIDIA Ampere GPU Architecture's features.
- PTX ISA
- This guide provides detailed instructions on the use of PTX, a low-level parallel thread execution virtual machine and instruction set architecture (ISA). PTX exposes the GPU as a data-parallel computing device.
- Developer Guide for Optimus
- This document explains how CUDA APIs can be used to query for GPU capabilities in NVIDIA Optimus systems.
- Video Decoder
- NVIDIA Video Decoder (NVCUVID) is deprecated. Instead, use the NVIDIA Video Codec SDK (https://developer.nvidia.com/nvidia-video-codec-sdk).
- PTX Interoperability
- This document shows how to write PTX that is ABI-compliant and interoperable with other CUDA code.
- Inline PTX Assembly
- This document shows how to inline PTX (parallel thread execution) assembly language statements into CUDA code. It describes available assembler statement parameters and constraints, and the document also provides a list of some pitfalls that you may encounter.
- CUDA Occupancy Calculator
- The CUDA Occupancy Calculator allows you to compute the multiprocessor occupancy of a GPU by a given CUDA kernel.
CUDA API References
Cuda For Mac
- CUDA Runtime API
- The CUDA runtime API.
- CUDA Driver API
- The CUDA driver API.
- CUDA Math API
- The CUDA math API.
- cuBLAS
- The cuBLAS library is an implementation of BLAS (Basic Linear Algebra Subprograms) on top of the NVIDIA CUDA runtime. It allows the user to access the computational resources of NVIDIA Graphical Processing Unit (GPU), but does not auto-parallelize across multiple GPUs.
- NVBLAS
- The NVBLAS library is a multi-GPUs accelerated drop-in BLAS (Basic Linear Algebra Subprograms) built on top of the NVIDIA cuBLAS Library.
- nvJPEG
- The nvJPEG Library provides high-performance GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications.
- cuFFT
- The cuFFT library user guide.
- cuRAND
- The cuRAND library user guide.
- cuSPARSE
- The cuSPARSE library user guide.
- NPP
- NVIDIA NPP is a library of functions for performing CUDA accelerated processing. The initial set of functionality in the library focuses on imaging and video processing and is widely applicable for developers in these areas. NPP will evolve over time to encompass more of the compute heavy tasks in a variety of problem domains. The NPP library is written to maximize flexibility, while maintaining high performance.
- NVRTC (Runtime Compilation)
- NVRTC is a runtime compilation library for CUDA C++. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx, and linked with other modules by cuLinkAddData of the CUDA Driver API. This facility can often provide optimizations and performance not possible in a purely offline static compilation.
- Thrust
- The Thrust getting started guide.
- cuSOLVER
- The cuSOLVER library user guide.
Miscellaneous
- CUDA Samples
- This document contains a complete listing of the code samples that are included with the NVIDIA CUDA Toolkit. It describes each code sample, lists the minimum GPU specification, and provides links to the source code and white papers if available.
- CUDA Demo Suite
- This document describes the demo applications shipped with the CUDA Demo Suite.
- CUDA on WSL
- This guide is intended to help users get started with using NVIDIA CUDA on Windows Subsystem for Linux (WSL 2). The guide covers installation and running CUDA applications and containers in this environment.
- Multi-Instance GPU (MIG)
- This edition of the user guide describes the Multi-Instance GPU feature of the NVIDIA® A100 GPU.
- CUPTI
- The CUPTI-API. The CUDA Profiling Tools Interface (CUPTI) enables the creation of profiling and tracing tools that target CUDA applications.
- Debugger API
- The CUDA debugger API.
- GPUDirect RDMA
- A technology introduced in Kepler-class GPUs and CUDA 5.0, enabling a direct path for communication between the GPU and a third-party peer device on the PCI Express bus when the devices share the same upstream root complex using standard features of PCI Express. This document introduces the technology and describes the steps necessary to enable a GPUDirect RDMA connection to NVIDIA GPUs within the Linux device driver model.
- vGPU
- vGPUs that support CUDA.
Tools
- NVCC
- This is a reference document for nvcc, the CUDA compiler driver. nvcc accepts a range of conventional compiler options, such as for defining macros and include/library paths, and for steering the compilation process.
- CUDA-GDB
- The NVIDIA tool for debugging CUDA applications running on Linux and QNX, providing developers with a mechanism for debugging CUDA applications running on actual hardware. CUDA-GDB is an extension to the x86-64 port of GDB, the GNU Project debugger.
- CUDA-MEMCHECK
- CUDA-MEMCHECK is a suite of run time tools capable of precisely detecting out of bounds and misaligned memory access errors, checking device allocation leaks, reporting hardware errors and identifying shared memory data access hazards.
- Compute Sanitizer
- The user guide for Compute Sanitizer.
- Nsight Eclipse Plugins Installation Guide
- Nsight Eclipse Plugins Installation Guide
- Nsight Eclipse Plugins Edition
- Nsight Eclipse Plugins Edition getting started guide
- Nsight Compute
- The NVIDIA Nsight Compute is the next-generation interactive kernel profiler for CUDA applications. It provides detailed performance metrics and API debugging via a user interface and command line tool.
- Profiler
- This is the guide to the Profiler.
- CUDA Binary Utilities
- The application notes for cuobjdump, nvdisasm, and nvprune.
White Papers
- Floating Point and IEEE 754
- A number of issues related to floating point accuracy and compliance are a frequent source of confusion on both CPUs and GPUs. The purpose of this white paper is to discuss the most common issues related to NVIDIA GPUs and to supplement the documentation in the CUDA C Programming Guide.
- Incomplete-LU and Cholesky Preconditioned Iterative Methods
- In this white paper we show how to use the cuSPARSE and cuBLAS libraries to achieve a 2x speedup over CPU in the incomplete-LU and Cholesky preconditioned iterative methods. We focus on the Bi-Conjugate Gradient Stabilized and Conjugate Gradient iterative methods, that can be used to solve large sparse nonsymmetric and symmetric positive definite linear systems, respectively. Also, we comment on the parallel sparse triangular solve, which is an essential building block in these algorithms.
![Nvidia Cuda Emulator For Mac Os X Nvidia Cuda Emulator For Mac Os X](/uploads/1/2/6/5/126581519/866545056.jpg)
Application Notes
- CUDA for Tegra
- This application note provides an overview of NVIDIA® Tegra® memory architecture and considerations for porting code from a discrete GPU (dGPU) attached to an x86 system to the Tegra® integrated GPU (iGPU). It also discusses EGL interoperability.
Compiler SDK
- libNVVM API
- The libNVVM API.
- libdevice User's Guide
- The libdevice library is an LLVM bitcode library that implements common functions for GPU kernels.
- NVVM IR
- NVVM IR is a compiler IR (internal representation) based on the LLVM IR. The NVVM IR is designed to represent GPU compute kernels (for example, CUDA kernels). High-level language front-ends, like the CUDA C compiler front-end, can generate NVVM IR.