The CUDA™ Toolkit is a C language development environment for CUDA-enabled GPUs. The CUDA development environment includes:
- nvcc C compiler
- CUDA FFT and BLAS libraries for the GPU
- Profiler
- gdb debugger for the GPU (alpha available in March, 2008)
- CUDA runtime driver (now also available in the standard NVIDIA GPU driver)
- CUDA programming manual
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
- Parallel bitonic sort
- Matrix multiplication
- Matrix transpose
- Performance profiling using timers
- Parallel prefix sum (scan) of large arrays
- Image convolution
- 1D DWT using Haar wavelet
- OpenGL and Direct3D graphics interoperation examples
- CUDA BLAS and FFT library usage examples
- CPU-GPU C- and C++-code integration
- Binomial Option Pricing
- Black-Scholes Option Pricing
- Monte-Carlo Option Pricing
- Parallel Mersenne Twister (random number generation)
- Parallel Histogram
- Image Denoising
- Sobel Edge Detection Filter
- MathWorks MATLAB® Plug-in (click here to download)
New SDK Samples in CUDA version 1.1 is available now.
- Standard C programming language enabled on a GPU
- Unified hardware and software solution for parallel computing on CUDA-enabled NVIDIA GPUs
- CUDA compatible GPUs range from lower power notebook GPUs to high performance, multi-GPU systems
- 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)
- Dedicated CUDA driver for computing
- Optimized direct upload and download path from the CPU to CUDA-enabled GPU
- CUDA driver interoperates with OpenGL and DirectX graphics drivers
- Support for Linux 32/64-bit and Windows XP 32/64-bit operating systems
- Direct driver and assembly level access through CUDA for research and language development
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