IntoductionThe Nvidia Tesla C2070 and M2090 Computing Processors fuel the transition to parallel computing and bring the performance of a small cluster to the desktop. NVIDIA CUDA development tools provide four key components CUDA Driver Vendor : nVidia Tesla CUDA Driver Introduction CUDA drivers are needed to drive the massively parallel Nvidia GPU’s. The CUDA drivers are already installed and loaded with the OS kernel. Version: NVIDIA Driver 304.54 for Linux CUDA Toolkit Introduction The CUDA Toolkit is a C language development environment for CUDA-enabled GPUs. The CUDA development environment includes * nvcc C compiler Release: 5.0 NVIDIA CUDA Compiler Introduction CUDA is the compute engine in NVIDIA graphics processing units or GPUs, that is accessible to software developers through industry standard programming languages. Programmers use C for CUDA using the nvcc compiler. Purpose of nvcc This compilation trajectory involves several splitting, compilation, preprocessing, and merging steps for each CUDA source file, and several of these steps are subtly different for different modes of CUDA compilation (such as compilation for device emulation, or the generation of fat device code binaries). It is the purpose of the CUDA compiler driver nvcc to hide the intricate details of CUDA compilation from developers. Vendor : nVidia Tesla Compiling with CUDA In order to use nvcc to compile cuda programs, following environmental setup is requried: CSH – Shell: Add the following lines in your .cshrc file setenv CUDA_INSTALL_PATH /usr/local/cuda-5.0 Run the command source .cshrc BASH – Shell: Add the following lines in your .bashrc file export CUDA_INSTALL_PATH=/usr/local/cuda-5.0 Run the command source .bashrc nvcc filename.cu CUDA SDK Introduction The CUDA Developer SDK provides examples with source code and utilities to help you get started writing software with CUDA. The SDK includes dozens of code samples covering a wide range of applications like * Parallel bitonic sort To run the sample cuda programs For C Shell Add the following line in .cshrc file set path=(/usr/local/cuda-5.0/samples/bin/linux/release $path) Run the command source .cshrc For Bash shell Add the following line in .bashrc file export PATH=/usr/local/cuda-5.0/samples/bin/linux/release:$PATH Run the command source .bashrc To run a sample program histogram The source files are located in /usr/local/cuda-5.0/samples/3_Imaging 1. Nvidia CUDA Programming Guide 2. Nvidia CUDA Best Practices Guide 3. Nvidia CUDA Reference Manual 4. Nvidia CUDA GDB User Manual Report Problems to: If you encounter any problem in using CUDA Development Tools please report to SERC helpdesk at the following email address helpdesk.serc@iisc.ac.in or contact System Administrators in 109 (SERC). |