Profit and Loss Modeling on GPUs with ISO C++ Language Parallelism
The previous post How to Accelerate Quantitative Finance with ISO C++ Standard Parallelism demonstrated how to write a Black-Scholes simulation using ISO C++ standard parallelism with the code found in the /NVIDIA/accelerated-quant-finance GitHub repo. This approach enables you to productively write code that is both concise and portable. Using solely standard C++, it’s possible to write an application that can be run in parallel on a modern, multicore CPU or on a GPU without modification. This post builds a more complex model, starting from the previously developed parallel Black-Scholes code, and optimizes it to use the benefits of the GPU, while remaining in standard C++.
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Read MoreHow to Accelerate Quantitative Finance with ISO C++ Standard Parallelism
Quantitative finance libraries are software packages that consist of mathematical, statistical, and, more recently, machine learning models designed for use in quantitative investment contexts. They contain a wide range of functionalities, often proprietary, to support the valuation, risk management, construction, and optimization of investment portfolios.
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The new hardware developments in NVIDIA Grace Hopper Superchip systems enable some dramatic changes to the way developers approach GPU programming. Most notably, the bidirectional, high-bandwidth, and cache-coherent connection between CPU and GPU memory means that the user can develop their application for both processors while using a single, unified address space.
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