CUDA-based Volume Rendering in IGT

Nobuhiko HataBenjamin Grauer

Objective

• To perform 4D image guided operation using Slicer

• To develop fast GPU-accelerated volume rendering in VTK

• To integrate fast GPU-accelerated image processing in ITK

NVIDIA CUDA™ Technology• Standard C programming language enabled on a GPU• Unified hardware and software solution for parallel computing on

CUDA-enabled NVIDIA GPUs• CUDA-enabled GPUs support the Parallel Data Cache and Thread

Execution Manager• Standard numerical libraries for FFT (Fast Fourier Transform) and

BLAS (Basic Linear Algebra Subroutines)• Optimized direct upload and download path from the CPU to

CUDA-enabled GPU• Support for Linux 32/64-bit, Windows XP 32/64-bit, (Mac OS X) • http://www.nvidia.com/object/cuda_home.html

• Project wiki page– http://www.slicer.org/slicerWiki/index.php/

Slicer3:Volume_Rendering_With_Cuda

• Codes– http://www.na-mic.org/svn/Slicer3/branches/

cuda/

Features• Matrix multiplication, Matrix transpose• Performance profiling using timers• Parallel prefix sum (scan) of large arrays• Image convolution• CUDA BLAS and FFT library usage examples• Binomial Option Pricing• Black-Scholes Option Pricing• Monte-Carlo Option Pricing• Parallel Mersenne Twister (random number generation)• Parallel Histogram• Sobel Edge Detection Filter• MathWorks MATLAB® Plug-in