Top Rated NVIDIA CUDA GL Alternatives
39 NVIDIA CUDA GL Reviews
It is faster than its competitors. It has better support and integration for almost all the required libraries to run the deep neural network task required for my professional work. Review collected by and hosted on G2.com.
I had not found any issues while working on my previous project. As a human tendency, I would like more advanced version to further speed the computation power. Review collected by and hosted on G2.com.
Being based on C is useful, especially in regards to extensions and the learning curve compared to other alternates I had in mind. The former factor made it very fast as well which meant faster data transfers. Furthermore, I could configure the cache as per my demands because of the memory specs. Review collected by and hosted on G2.com.
I do not particularly like the non-opensource factor of this product, which while understandable makes me look towards using other tools so that I may have more control over my application. Besides that, the operational use case was above satisfactory. Review collected by and hosted on G2.com.
It increases computing performance by using GPU. GPU's has many computing unit, much more than cpu. cuda is using this feature improve multi-threading algorithms Review collected by and hosted on G2.com.
I think it could be more simple. I got a lot of problems understanding it. It could be more simple. It could be easy to understand. More libraries for different languages could be provided. Review collected by and hosted on G2.com.
Nvidia CUDA toolkit is easy to install and has a seamless work environment. It helps to deploy our model in an embedded system using high-end GPUs which not only increase the processing speed but also enhance the user experience. Also, it provides many libraries and deep learning support. Review collected by and hosted on G2.com.
I feel NVIDIA CUDA can work for and provide fallback support for its older versions as well so that users going with older versions also get proper help from its technical community. Review collected by and hosted on G2.com.
The advantage of cuda basically, it has all hardware enabled CUDA features that are going to run much faster than comparable programming in other code environments. Review collected by and hosted on G2.com.
I did not understand wraps thread blocks and grids also their relationship and heirarchy. Review collected by and hosted on G2.com.
It supported trending deep learning frameworks like PyTorch and Tensorflow, also older frameworks like Caffe and MXNet. Review collected by and hosted on G2.com.
Some of the function requires high-end hardware, such as the Nvidia-RTX series. Review collected by and hosted on G2.com.
I used CUDA programming for using GPU in multi threading problem. Review collected by and hosted on G2.com.
I used CUDA programming for using GPU in multi threading problem.
The best part is the syntax is similar to C which helped me onboard easily Review collected by and hosted on G2.com.
I was working on an image processing project which is time critical. Processed that images realtime thanks to this. Review collected by and hosted on G2.com.
I struggled a bit at the first in search of proper documentation or a tutorial. Review collected by and hosted on G2.com.
Tools like TensorRT , which speeds up the whole inference Review collected by and hosted on G2.com.
There is no straight forward installation technique in an EC2 instance, have to go through a lot of tries Review collected by and hosted on G2.com.
It works amazing with openGL, and also is great at parallel computing Review collected by and hosted on G2.com.
I havebt really used it that much to say anything negative but the market has cheaper ones Review collected by and hosted on G2.com.
