As a result, it can sometime be better to recompute a value than to save it to memory and reload it later. The GPU can easily execute many math instructions in the time it takes to request and receive one number stored in GPU memory.
![anaconda prompt anaconda prompt](https://user-images.githubusercontent.com/21064099/34772662-9d459fd2-f62a-11e7-96a2-a3369c741ba9.png)
These algorithms take advantage of the GPU’s high math throughput, and its ability to queue up memory access in the background while doing math operations on other data at the same time. What’s the commonality to all these successful use cases? Broadly speaking, applications ready for GPU acceleration have the following features:įor every memory access, how many math operations are performed? If the ratio of math to memory operations is high, the algorithm has high arithmetic intensity, and is a good candidate for GPU acceleration.
Anaconda prompt how to#
Given how quickly the field is moving, it is a good idea to search for new GPU accelerated algorithms and projects to find out if someone has figured out how to apply GPUs to your area of interest.
![anaconda prompt anaconda prompt](https://i.stack.imgur.com/R6cuy.jpg)
Fortunately, Anaconda Distribution makes it easy to get started with GPU computing with several GPU-enabled packages that can be installed directly from our package repository.
Anaconda prompt software#
However, building GPU software on your own can be quite intimidating. In addition, GPUs are now available from every major cloud provider, so access to the hardware has never been easier. Computational needs continue to grow, and a large number of GPU-accelerated projects are now available. GPU computing has become a big part of the data science landscape.