SPEED UP YOUR Randomness-INTENSIVE WORKLOADS 

Meet Quside's Randomness Acceleration Platform Powered by Quside's Chip

Accelerate Now Your Results and Experiments in C++ and Python 

Built upon Quside’s QUANTUM RANDOMNESS technology

At Quside, we use hardware-accelerated, high-throughput, highest-quality random number probability distribution generator technology to accelerate our customers' randomness-intensive workloads. Building on our proprietary phase-diffusion quantum random number generation technology, we created a data center-grade, ready-to-use solution as a PCIe accelerator card that provides random number probability distributions with high throughput and high quality.

 Use our libraries to enable access to random number distributions, and obtain a speedup in your randomness-intensive workloads with minor changes in your design and development workflows, using our libraries for C++ and Python, together with series of examples and use cases, including Jupyter notebooks. 

Applications

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FINANCE

Speed-up Monte Carlo
Simulations up to 400%.

Optimize larger
portfolios,faster.

Accelerate risk
assessment calculations.

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PHARMA/SCIENCE 

Speed-up stochastic
models simulations.

Optimize computational
chemistry calculations.

Accelerate drug
discovery processes.

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logistics / OPERATIONS

Speed-up scheduling and
dispatching simulations.

Optimize inventory
and stock estimations.

Accelerate strategic and tactical
planning and forecasting.

RANDOMNESS ACCELERATION PLATFORM 

Components

Quside PCIe Randomness Acceleration cards
   >> PCIe Gen2 x4, with up to 10 Gbps random distribution streams (32- and 64-bit floating-point, uniform and normal)
   >> Leverage the whole computing power of your device by outsourcing the randomness generation subroutines
   >> Using the highest seed quality, with up to 1 Gbps entropy rate

C++ and Python libraries to access the Randomness Acceleration
   >> Get the highest speed-ups by integrating with the native C++ libraries
   >> Get an instantaneous speed-up in your Python models using our adapted version of NumPy
   >> Ready-to-use examples for C++, Python and Python Jupyter notebooks

Consumption models

Private Cloud
   >> Deploy the PCIe Randomness Acceleration Cards in a VMWare-virtualized standard server
   >> Ready to use OVF (Open Virtualization Format) appliance with pre-installed libraries and dependencies, including examples and a Jupyter notebook environment
   >> Full Randomness Acceleration Platform support

Stay tuned for further updates.

START ACCELERATING

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