nvcc.pdf v4.0 1
January 2011
Introduction
Overview
CUDA programming model
The CUDA Toolkit targets a class of applications whose control part runs as a
process on a general purpose computer (Linux, Windows), and which use one or
more NVIDIA GPUs as coprocessors for accelerating SIMD parallel jobs. Such
jobs are „self- contained‟, in the sense that they can be executed and completed by a
batch of GPU threads entirely without intervention by the „host‟ process, thereby
gaining optimal benefit from the parallel graphics hardware.
Dispatching GPU jobs by the host process is supported by the CUDA Toolkit in
the form of remote procedure calling. The GPU code is implemented as a collection
of functions in a language that is essentially „C‟, but with some annotations for
distinguishing them from the host code, plus annotations for distinguishing
different types of data memory that exists on the GPU. Such functions may have
parameters, and they can be „called‟ using a syntax that is very similar to regular C
function calling, but slightly extended for being able to specify the matrix of GPU
threads that must execute the „called‟ function. During its life time, the host process
may dispatch many parallel GPU tasks. See Figure 1.
CUDA sources
Hence, source files for CUDA applications consist of a mixture of conventional
C++ „host‟ code, plus GPU „device‟ (i.e. GPU-) functions. The CUDA compilation
trajectory separates the device functions from the host code, compiles the device
functions using proprietary NVIDIA compilers/assemblers, compiles the host code
using a general purpose C/C++ compiler that is available on the host platform, and
afterwards embeds the compiled GPU functions as load images in the host object
file. In the linking stage, specific CUDA runtime libraries are added for supporting
remote SIMD procedure calling and for providing explicit GPU manipulation such
as allocation of GPU memory buffers and host-GPU data transfer.
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 device code repositories). It is the purpose of the
CUDA compiler driver nvcc to hide the intricate details of CUDA compilation from
developers. Additionally, instead of being a specific CUDA compilation driver,
mbb_3332012-09-18不错，以前下载了个2.0的，现在有4.0的了，不错
