Run More Detailed VaR And xVA Computations, Faster
Build more detailed risk and interest rate models, run scenarios more frequently, and reduce both compute infrastructure capital expenses and compute operating costs.
Cut risk modeling costs while increasing model sophistication
Your VaR, xVA, and interest rate models are essential for market risk estimation, but their Monte Carlo computations are expensive and slow.
Signaloid's UxHw technology delivers up to 1000x speedups, enabling you to:
Reduce computing infrastructure costs
Run more detailed risk models
Increase analysis frequency
Turn overnight jobs into minute-long tasks
Up to
0x
speedup
Up to
0%
reduction in software implementation cost for compute-intensive applications*
Up to
0%
reduction in software implementation cost for graphical/interactive applications*
Solutions
Choose between running computations on our cloud-based compute engine or on your current on-premises infrastructure.
Example Performance Validation
Speed up your existing AWS EC2 or on-premises deployments by 2x–8x while achieving the same level of accuracy by deploying Signaloid’s UxHw virtualization layer on top of existing hardware. Achieve even greater speedups using UxHw accelerator cards. Give your existing optimized quant libraries an additional boost, by integration with your existing compilers and numerics libraries.
Technology Explainers
Signaloid's UxHw technology is the easiest way to implement solutions to computing problems currently addressed using Monte Carlo methods. It is also the fastest way to execute these workloads, providing speedups of 2x–8x (or more) across relevant quantitative finance workloads. UxHw achieves these capabilities through its facility for deterministic and efficient arithmetic on probability distributions. And because it is available as either a virtual CPU or as hardware accelerator modules, adoption is easy: it complements existing CPU and GPU hardware and optimizations already implemented in your quantitative finance libraries. The technology explainers below provide further details on how this technology benefits Quantitative Finance applications, from the underlying mathematics to the engineering implementations.
