- Download Apple NVIDIA Driver for Mac to improve the stability of your NVIDIA card with this update.
- Download this app from Microsoft Store for Windows 10. See screenshots, read the latest customer reviews, and compare ratings for NVIDIA Control Panel.
- CUDA Mac Driver Latest Version: CUDA 418.163 driver for MAC Release Date: Previous Releases: CUDA 418.105 driver for MAC Release Date: CUDA 410.130 driver for MAC Release Date: CUDA 396.148 driver for MAC Release Date: CUDA 396.64 driver for MAC Release Date: CUDA 387.178 driver for MAC.
Moonlight allows you to play your PC games on almost any device, whether you're in another room or miles away from your gaming rig. Moonlight (formerly Limelight) is an open source implementation of NVIDIA's GameStream protocol. We implemented the protocol used by the NVIDIA Shield and wrote a set of 3rd party clients.
CUDA Toolkit Documentation - v11.1.1 (older) - Last updated October 29, 2020 - Send Feedback
- Release Notes
- The Release Notes for the CUDA Toolkit.
- EULA
- The End User License Agreements for the NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA Display Driver, and NVIDIA NSight (Visual Studio Edition).
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 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 Runtime API
- Fields in structures might appear in order that is different from the order of declaration.
- CUDA Driver API
- Fields in structures might appear in order that is different from the order of declaration.
- 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.
- CUB
- The user guide for CUB.
- CUDA C++ Standard
- The API reference for libcu++, the CUDA C++ standard library.
- 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.
PTX Compiler API References
- PTX Compiler APIs
- This guide shows how to compile a PTX program into GPU assembly code using APIs provided by the static PTX Compiler library.
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.
- CUDA Compatibility
- This document describes CUDA Compatibility, including CUDA Enhanced Compatibility and CUDA Forward Compatible Upgrade.
- 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 Mac, 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.
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.
If you are bored of your old windows pc, the Crotona, the same old UI, the blue screen of death. Then you are at the correct place, I have created a video on YouTube in which I showed you a step by step tutorial on how to install mackintosh on your non mac pc or in my case an AMD computer if you want to watch that video I will link it right over here, for now I will include the important links that we need while installing the macOS on our AMD computer
My Pc specification for AMD Sierra
Processor – AMD Fx-6300 Black Edition 6 cores
Ram – 4 GB
Motherboard – GIGABYTE GA78LMT-USB3
Geforce Mac
Graphics Card – NVIDIA GTX 750 Ti
1) Checking for the compatibility
Not a whole lot of processors especially the AMD ones are compatible with the hackintosh Operating system but thankfully we have a website from where we can find out the compatibility of the processor to check just visit http://cpuboss.com/ and search for your processor (mine is the FX-6300 6 cores black edition) scroll down to features and if your CPU is compatible you will get a 1 instruction set if it is there then follow the rest of the guide if not then you cannot install the Sierra.
2) Downloading the Python Installer
If you are using the cmd method to download the dmg file and creating a bootable usb for your hackintosh machine then you will have to install the latest python drivers on your pc , you can download then from here – https://www.python.org/
3) Downloading the Sierra (dmg) file
Links to download the Sierra (12.16.4) this is the same one that i used in the video
– SierraAMDv5.2 – http://bit.ly/2qJ2vLb
4) Making the USB boot-able
You will have to download TransMac software this will make the pen-drive bootable with the macOS, this will also make the pendrive format into the GUID format which is readable by macOS.
Link to download the software – https://www.acutesystems.com/scrtm.htm
5) Changing the Bios Settings
There are a lot of tweaks that you can do to make the installation smoother but basically you will only need to make this one change
INTEGRATED PERIPHERALS – ON CHIP SATA TYPE – AHCI
this is basically it if you want then you can disable or enable the 3.0 USB hubs if you want but that is not mandatory
Download Nvidia Drivers For Mac Os X
6) Boot Arguments
You will have to add the following boot arguments while installing the macOS,
please note – you will have to change it later when you have installed the macOS and installed the clover bootloader
boot arguments = ncpi=0x3000 -v nv_disable=1
7) Installing NVIDIA Web Drivers
to install web drivers you will have to download the necessary web drivers for your graphic card mine is the NVIDIA GTX 750TI
Link – https://www.tonymacx86.com/nvidia-drivers/
add the following command ( this is enable the graphic card, we previously stopped them in step 6)
nvda_drv=1
That’s all folks !