Please Note: There is a recommended patch for CUDA 7.0 which resolves an issue in the cuFFT library that can lead to incorrect results for certain inputs sizes less than or equal to 1920 in any dimension when cufftSetStream is passed a non-blocking stream (e.g., one created using the cudaStreamNonBlocking flag of the CUDA Runtime API or the CUSTREAMNONBLOCKING flag of the CUDA. NVIDIA just released its GPGPU technology i.e. The infrastructure for performing general purpose computing on its graphics cards, for Mac OS X. This technology is called “Compute Unified Device Architecture” or simply CUDA and it essentially provides a programmer with a C-compiler that creates executables that run on their GPUs. CUDA also includes some common optimized libraries like BLAS.

This document introduces CUDA‐GDB, the NVIDIA® CUDA™ debugger, and describes what is new in version 4.2. What is CUDA-GDB? CUDA‐GDB is the NVIDIA tool for debugging CUDA applications running on Linux and Mac. CUDA‐GDB is an extension to the x86‐64 port of GDB, the GNU Project debugger. CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded. 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.

Introduction

So you’re a Mac user and a creative, plus it’s 2020. GPU hardware acceleration is a thing, and it has been a thing for quite some time now. You’ve heard of it, but you’ve also heard lots of confusing terms bandied around, GPGPU, CUDA, real-time rendering, OpenCL, Mercury Graphics Engine, Metal. What does it all mean? Well, pull up a chair because Create Pro is here to break it all down.

What is GPGPU?

Let’s start at the beginning. When we talk about GPU acceleration we’re talking about GPGPU, that is, general-purpose computing on graphics processing units. So now we know what GPGPU stands for, what does it actually do? GPGPU takes advantage of software frameworks such as CUDA (Nvidia), OpenCL (open source) and Metal (Apple) to accelerate certain functions in your favourite creative software with the goal of making your work life quicker and easier. This could be a reduction in rendering time, better real-time previewing, or access to higher-quality effects.

If you want to dig deeper, GPGPU is the act of a GPU performing tasks normally handled by the CPU (processor). Before GPGPU, data could be passed from the CPU to the GPU, but not back. GPGPU changes this and allows data to flow in both directions. This bidirectional processing is hugely beneficial in a wide variety of tasks relating to video, still image and 3D graphics.

The GPGPU frameworks you have access to depend on the GPU you have in your Mac. Nvidia cards support CUDA and OpenCL, AMD cards support OpenCL and Metal.

2015 Article, what’s new?

You may remember, we first published an article on this very topic back in 2015, some of it still stands, but a lot has changed and it’s definitely time for an update. GPGPU is still the way to go if you’re a creative professional, but the advice is now a little different.

So what exactly has changed? Well, unfortunately, Nvidia has dropped GPU support for Mac systems. This means that starting with macOS 10.14 Mojave, Nvidia GPUs are no longer compatible with Mac systems (this doesn’t include Nvidia cards shipped with Mac systems, such as those in retina MacBooks). To put it bluntly, if you’re required to work on macOS 10.14 or newer, Nvidia cards are out of the question for you. This doesn’t mean they should be completely counted out though, there are still some situations where Nvidia cards remain the best choice.

The second big change since 2015 has been Apple’s own GPGPU framework, Metal. Since being debuted in 2014 Metal has come into its own as a real player in the GPGPU scene. As of 2020, it’s now the best choice for many apps/situations. Metal is currently only supported by AMD cards only, so this is something to keep in mind.

AMD vs. Nvidia in 2020

Back in 2015, there was a huge performance gap between Nvidia and AMD. If you read our previous article our recommendation was “In our view, Nvidia GPUs (especially newer ones) are usually the best choice for users, with built-in CUDA support as well as strong OpenCL performance for when CUDA is not supported. The only situation in which we would recommend an AMD GPU to professionals is when they are exclusively using apps that support OpenCL and have no CUDA option”. Nowadays, whilst AMD is still ever so slightly behind when it comes to raw GPU power, the two are now much more closely aligned.

So, what was once an easy decision has been made a little more difficult. Fortunately (or in some cases, unfortunately) for us, Nvidia has made this decision a little easier by cutting support for their cards in newer versions of macOS. This means that for most, the choice is between AMD and it’s ease of use and Metal prowess, or figuring out whether the hoops you’re required to jump through make the potential benefits of using an Nvidia card are worth it.

Let’s take a look at the current strengths of each GPGPU framework to see what factors might impact your choice of GPU.

CUDA/Nvidia

CUDA, despite not currently being supported in macOS, is as strong as ever. The Nvidia cards that support it are powerful and CUDA is supported by the widest variety of applications (see full table below for more info).

Something to keep a note of is that CUDA, unlike OpenCL, is Nvidia’s own proprietary framework. This means that unlike other open-source frameworks, CUDA is constantly being worked on by its own team and Nvidia are constantly providing resources to further this development. Having this consistent and well-resourced team is certainly positive for CUDA.

So which users should go for Nvidia cards? In our opinion, due to compatibility issues, we would only recommend Nvidia cards to users who use applications that support CUDA exclusively. Some popular apps and plugins that only support CUDA are; Adobe SpeedGrade, Avid Media Composer & Motion Graphics, RED Giant Effects Suite & Magic Bullet Looks, The Foundry HIERO, NUKE, NUKEX & Mari, as well as industry favourite OTOY Octane Render.

If you are going to use an Nvidia card, please keep in mind the macOS compatibility issues previously mentioned, if you have any questions, feel free to ask in the comments below.

OpenCL

OpenCL, open-source and now widely supported, bolstered by the great line up of AMD cards currently available is a very compatible and powerful GPGPU framework currently. OpenCL is available to both AMD and Nvidia GPUs.

Unlike CUDA, the fact that OpenCL is open-source means it doesn’t necessarily have the same consistent development team or funding as CUDA, but with this in mind, it has certainly achieved a lot with what it does have at its disposal.

It would be remiss of us to neglect to mention that Metal has in many ways rendered OpenCL a little irrelevant. Metal is supported by the same AMD cards that OpenCL performs best on and in most cases, when both frameworks are supported, Metal is the best option. However, there are a few select apps, such as Capture One, which support only OpenCL, so the framework does have a little life in it still.

Metal

The new kid on the block, but certainly not one to underestimate, Metal has been the rising star of the GPGPU scene in the last few years. Metal has sought to combine OpenCL and OpenGL in a single low-level API. As Metal is embedded within macOS at the lowest level, it’s super-efficient and provides huge performance benefits.

Like CUDA, Metal has its own consistent development team and as part of Apple has access to huge resources, this means steady updates and more great things to come in the future.

Currently, you’ll need an AMD card to take advantage of Metal in macOS. This isn’t a bad thing though, because the current crop of AMD cards is nothing to scoff at.

Metal currently provides the best performance boosts across the Adobe suite, Blackmagic DaVinci Resolve and Final Cut Pro X. With this in mind, if you’re using any of these apps, you should be using Metal. For full compatibility check the table below.

As you can see from the table above, CUDA is the GPGPU framework with the widest range of support, however as macOS now does not support Nvidia GPUs, this can be a slightly moot point.

Final Thoughts

In our mind it’s like this, Metal is the best option for Mac users if available. If you have to choose between CUDA and OpenCL, CUDA will probably have better performance, but setting up with AMD will be easier and more future proof. Remember, your app may adopt Metal in the future, as we’re sure more and more apps will.

Nvidia Cuda Download Windows 10

Here are a few simple rules if you’re stuck, remember you can always leave questions in the comments below:

If your app of choice supports metal, grab an AMD GPU and run with Metal. This will give you the best performance and ease of use, so is a no brainer.

Nvidia Drivers For Mac

If your app supports OpenCL, but not metal, again grab an AMD GPU.

If your app only supports CUDA, or only CUDA and OpenCL, and performance is extremely important, consider running an older (pre-macOS 10.13.6) OS and using an Nvidia card. But keep compatibility issues in mind.

Blu-ray Master converters has adopted the NVIDIA® CUDA™ technology, which can improve video encoding/decoding performance on any computer with a CUDA-enabled GPU.

Nvidia Cuda Toolkit

What is CUDA?

CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing architecture, which enables GPU (graphics processing units) to handle complex computing issues. Using NVIDIA's GPU to convert Blu-ray/DVD/video can reserve more spaces and resources of your computer CPU that can be used for other applications and tasks.

Our software can only accelerate the conversion from BD to the video with H.264 codec.

Cuda Driver For Mac

Supported Graphics Card:

Nvidia Cuda Driver For Mac

Note: the CUDA-enabled GPU should have a minimum of 256MB of local graphics memory. All the Supported Graphics Card we provide above is just the reference. For the specific acceleration statement with NVIDIA CUDA technology, users should take NVIDIA official site as the standard.