Financial institutions rely on value at risk (VaR), valuation adjustment (xVA), interest rate models, and more, to estimate market risk and to inform decisions. These models often involve compute-intensive Monte Carlo methods that require significant provisioning of computing resources and long-running computations.
The significant workload execution speedups enabled by integrating Signaloid’s UxHw technology into your technology stack allows you to reduce the capital expenses for your risk modeling workloads, implement more detailed models, and run your risk analyses more frequently. With up to 1000x speedups in relevant use cases, workloads that once ran overnight can now complete in minutes.
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.
