This sample evaluates fair call price for a given set of European options using the Monte Carlo approach, taking advantage of all CUDA-capable GPUs installed in the system. This sample use double precision hardware if a GTX 200 class GPU is present. The sample also takes advantage of CUDA 4.0 capability to supporting using a single CPU thread to control multiple GPUs

Key Concepts

Random Number Generator, Computational Finance, CURAND Library

Supported SM Architectures

SM 5.0 SM 5.2 SM 5.3 SM 6.0 SM 6.1 SM 7.0 SM 7.2 SM 7.5 SM 8.0 SM 8.6 SM 8.7 SM 8.9 SM 9.0

Supported OSes

Linux, Windows

Supported CPU Architecture

x86_64, armv7l

CUDA APIs involved

CUDA Runtime API

cudaStreamDestroy, cudaMalloc, cudaFree, cudaMallocHost, cudaSetDevice, cudaEventSynchronize, cudaGetDeviceProperties, cudaDeviceSynchronize, cudaEventRecord, cudaFreeHost, cudaMemset, cudaStreamSynchronize, cudaEventDestroy, cudaMemcpyAsync, cudaStreamCreate, cudaGetDeviceCount, cudaEventCreate

Dependencies needed to build/run

CURAND

Prerequisites

Download and install the CUDA Toolkit 12.5 for your corresponding platform. Make sure the dependencies mentioned in Dependencies section above are installed.

References (for more details)

whitepaper