High Performance Computing Solutions
Quside QRNGs empower a broad range of randomized algorithms
Some of the most relevant simulation and optimization workloads rely on stochastic processes, which require an ever-increasing source of high-quality, high-speed random numbers.
Current means to generate random numbers may introduce artifacts in highly parallel simulations, while also consuming valuable computing resources. In contrast to these pseudo-random generators, physical sources of randomness may also be used. However, today’s physical RNG devices are typically slow and unavailable in HPC environments.
The Quside Randomness Acceleration cards deliver high performance and efficient randomness generation for a broad class of randomized algorithms.
Our libraries include a modified python NumPy library to facilitate adoptions for analysts and developers in multiple industries. With this functionality, users can benefit from quantum randomness without changing their codes.
Use cases
RELATED POSTS
Invisibly, cryptography permeates through all current communication systems. The ability to guarantee that our messages are transmitted in an unaltered form to only those recipients of interest to us is critical for users to have peace of mind when it comes to relying on the communication channels we use.
From science and technology to finance and logistics, virtually every field that in one way or another must deal with highly complex problems eventually turns to a Monte Carlo method as a means of solving them.
The cloud computing service model today is established and almost all companies are embracing a strategy of “go to cloud”, both in part and in full. Depending on a company’s requirements it could be a public, private or hybrid cloud service.
