I have a very simple Toshiba Laptop with i3 processor. Also, I do not have any expensive graphics card. In the display settings, I see Intel(HD) Graphics as display adapter. I am planning to learn some cuda programming. But, I am not sure, if I can do that on my laptop as it does not have any nvidia's cuda enabled GPU.

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Learn how to add distressed textures to your next logo project in After Effects with the simple steps in this video tutorial. Download the textures for free! Found an answer on Adobe Forum, appereantly Adobe After Effects CS6 is badly optimized for GPU use, expect for Ray-Tracing 3D where it support Nvidia cards only, here is a quote from a guy that helped me understand it. GPU acceleration in Ae is so misunderstood. CUDA only accelerates one specific feature in Ae -- the ray-tracing renderer. Hi, my CUDA doesn't work in After Effects. The problems seems to occur in After Effects itself. Is there any fix and or is there an update coming for the nvidia's 900 series cards? And just to let you know, I got the latest drivers for same After Effects and Nvidia. That should do it. It may be necessary to remove a text file in the package for the After Effects application, I know I had to edit this file in order to get CUDA working on AE on my old system (which I no longer have access to.) But I would try simply removing the CUDA icon from System Preferences first.

In fact, I doubt, if I even have a GPU o_o

So, I would appreciate if someone can tell me if I can do CUDA programming with the current configuration and if possible also let me know what does Intel(HD) Graphics mean?


4 Answers

At the present time, Intel graphics chips do not support CUDA. It is possible that, in the nearest future, these chips will support OpenCL (which is a standard that is very similar to CUDA), but this is not guaranteed and their current drivers do not support OpenCL either. (There is an Intel OpenCL SDK available, but, at the present time, it does not give you access to the GPU.)

Newest Intel processors (Sandy Bridge) have a GPU integrated into the CPU core. Your processor may be a previous-generation version, in which case 'Intel(HD) graphics' is an independent chip.

Eugene SmithEugene Smith

Portland group have a commercial product called CUDA x86, it is hybrid compiler which creates CUDA C/ C++ code which can either run on GPU or use SIMD on CPU, this is done fully automated without any intervention for the developer. Hope this helps.

Link: http://www.pgroup.com/products/pgiworkstation.htm


If you're interested in learning a language which supports massive parallelism better go for OpenCL since you don't have an NVIDIA GPU. You can run OpenCL on Intel CPUs, but at best you can learn to program SIMDs.Optimization on CPU and GPU are different. I really don't think you can use Intel card for GPGPU.


Intel HD Graphics is usually the on-CPU graphics chip in newer Core i3/i5/i7 processors.

As far as I know it doesn't support CUDA (which is a proprietary NVidia technology), but OpenCL is supported by NVidia, ATi and Intel.


Not the answer you're looking for? Browse other questions tagged graphicscudagpugpu-programming or ask your own question.

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Powerful and reliable programming model and computing platform that allows you to make use of the power of the Graphics Processing Unit

NVIDIA CUDA Toolkit addresses a small group of developers and programmers working in C and C++ that are on the lookout for the official CUDA development environment from NVIDIA. With NVIDIA CUDA Toolkit, you can freely build GPU-accelerated application software projects.

Cuda 50 After Effects Downloads

First things first, CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the puissance of the graphics processing unit (GPU). CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared recollection. The on-chip shared recollection sanctions parallel tasks running on these cores to apportion data without sending it over the system recollection bus.

With NVIDIA CUDA Toolkit, the arrival of multicore CPUs and manycore GPUs designates that mainstream processor chips are now parallel systems. Furthermore, their parallelism perpetuates to scale with Moore's law. The challenge is to develop application software that transparently scales its parallelism to leverage the incrementing number of processor cores, much as 3D graphics applications transparently scale their parallelism to manycore GPUs with widely varying numbers of cores.

The CUDA parallel programming model is designed to surmount this challenge while maintaining a low learning curve for programmers habituated with standard programming languages such as C. At its core are three key abstractions - a hierarchy of thread groups, shared recollections, and barrier synchronization - that are simply exposed to the programmer as a minimal set of language extensions.

Overall, the NVIDIA CUDA Toolkit is the one and only environment to start your projects in if you want to take full advantage of this wonderful platform. Just make sure you have an NVIDIA graphics adapter that support CUDA technology before launching into this innovating climate of unlimited possibilities.

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New in NVIDIA CUDA Toolkit 10.0.130:
  • Compiler:
  • The CUDA-C and CUDA-C++ compiler, nvcc, is found in the bin/ directory. It is built on top of the NVVM optimizer, which is itself built on top of the LLVM compiler infrastructure. Developers who want to target NVVM directly can do so using the Compiler SDK, which is available in the nvvm/ directory.
Read the full changelog NVIDIA CUDA Toolkit was reviewed by Alexandru Pintilie

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