Achieve stunning acceleration for derivative pricing models
SciGPU is the parallel computing component of SciFinance®. Designed to take hand coding out of derivatives model development, SciFinance automatically generates C/C++/CUDA source code for any Monte Carlo-based pricing model.
SciGPU automatically generates NVIDIA GPU-enabled or OpenMP compliant source code for derivatives pricing models. No CUDA or parallel programming expertise is required.
SciComp Consulting, our highly-skilled and professional quantitative development team provides expert, cost-effective GPU programming and porting services for PDE (partial differential equation) pricing models.
- CUDA Monte Carlo pricing models:
- 30X-50X faster than serial code (single GPU, double precision)
- OpenMP-compliant code executes in the multi-processor environment
with nearly linear speed-up
- 3.9X faster than serial code on a quad-core PC, 22X on a 24 CPU workstation
Why use SciFinance for GPU computing?
- SciFinance automatically generates NVIDIA GPU-enabled or OpenMP-compliant source code for any Monte Carlo-based pricing model.
- No CUDA or parallel programming expertise required
- SciFinance automatically generates optimized CUDA code
- Optimized for double precision
- Mixed single/double precision code style option available
- Supports complex models with large parameter lists while respecting the CUDA kernel arguments limit
- Efficient CUDA local memory management
- Multi-GPU support
In order to quickly develop new structured products and vastly speed up Monte Carlo-based derivatives for its Software-as-a-Service (SaaS) customers, Reval uses SciFinance.
Need more information on SciFinance and GPU computing? Contact us >>