APPENDIX
Graphics and
Computing GPUs
J o h n Nickolls
Director of Architecture
NVIDIA
D a v i d Kirk
Chief Scientist
NVIDIA
Imagination is more
important than
knowledge.
Albert Einstein
On Science, 1930s
B
B.1 Introduction B-3
B.1 Introduction
is appendix focuses on the GPU —the ubiquitous graphics processing unit
in every PC, laptop, desktop computer, and workstation. In its most basic form,
the GPU generates 2D and 3D graphics, images, and video that enable Window-
based operating systems, graphical user interfaces, video games, visual imaging
applications, and video. e modern GPU that we describe here is a highly parallel,
highly multithreaded multiprocessor optimized for
visual computing . To provide
real-time visual interaction with computed objects via graphics, images, and video,
the GPU has a uni ed graphics and computing architecture that serves as both a
programmable graphics processor and a scalable parallel computing platform. PCs
and game consoles combine a GPU with a CPU to form
heterogeneous systems .
A Brief History of GPU Evolution
Fi een years ago, there was no such thing as a GPU. Graphics on a PC were
performed by a video graphics array (VGA) controller. A VGA controller was
simply a memory controller and display generator connected to some DRAM. In
the 1990s, semiconductor technology advanced su ciently that more functions
could be added to the VGA controller. By 1997, VGA controllers were beginning
to incorporate some three-dimensional (3D) acceleration functions, including
gr aphics processing
unit (GPU)
A p r o c e s s o r
optimized for 2D and 3D
graphics, video, visual
computing, and display.
visual computing A mix
of graphics processing
and computing that lets
you visually interact with
computed objects via
graphics, images, and
video.
heterogeneous
system
A s y s t e m
combining di erent
processor types. A PC is a
heterogeneous CPU–GPU
system.
B.1 Introduction B-3
B.2 GPU System Architectures B-7
B.3 Programming GPUs B-12
B.4 Multithreaded Multiprocessor Architecture B-25
B.5 Parallel Memory System B-36
B.6 Floating-point Arithmetic B-41
B.7 Real Stuff: The NVIDIA GeForce 8800 B-46
B.8 Real Stuff: Mapping Applications to GPUs B-55
B.9 Fallacies and Pitfalls B-72
B.10 Concluding Remarks B-76
B.11 Historical Perspective and Further Reading B-77
B-4 Appendix B Graphics and Computing GPUs
hardware for triangle setup and rasterization (dicing triangles into individual
pixels) and texture mapping and shading (applying “decals” or patterns to pixels
and blending colors).
In 2000, the single chip graphics processor incorporated almost every detail of
the traditional high-end workstation graphics pipeline and, therefore, deserved a
new name beyond VGA controller. e term GPU was coined to denote that the
graphics device had become a processor.
Over time, GPUs became more programmable, as programmable processors
replaced xed-function dedicated logic while maintaining the basic 3D graphics
pipeline organization. In addition, computations became more precise over time,
progressing from indexed arithmetic, to integer and xed point, to single-precision
oating-point, and recently to double-precision oating-point. GPUs have become
massively parallel programmable processors with hundreds of cores and thousands
of threads.
Recently, processor instructions and memory hardware were added to support
general purpose programming languages, and a programming environment was
created to allow GPUs to be programmed using familiar languages, including C
and C++. is innovation makes a GPU a fully general-purpose, programmable,
manycore processor, albeit still with some special bene ts and limitations.
GPU Graphics Trends
GPUs and their associated drivers implement the OpenGL and DirectX
models of graphics processing. OpenGL is an open standard for 3D graphics
programming available for most computers. DirectX is a series of Microso
multimedia programming interfaces, including Direct3D for 3D graphics. Since
these
application programming interfaces (APIs) have well-de ned behavior,
it is possible to build e ective hardware acceleration of the graphics processing
functions de ned by the APIs. is is one of the reasons (in addition to increasing
device density) why new GPUs are being developed every 12 to 18 months that
double the performance of the previous generation on existing applications.
Frequent doubling of GPU performance enables new applications that were
not previously possible. e intersection of graphics processing and parallel
computing invites a new paradigm for graphics, known as visual computing. It
replaces large sections of the traditional sequential hardware graphics pipeline
model with programmable elements for geometry, vertex, and pixel programs.
Visual computing in a modern GPU combines graphics processing and parallel
computing in novel ways that permit new graphics algorithms to be implemented,
and opens the door to entirely new parallel processing applications on pervasive
high-performance GPUs.
Heterogeneous System
Although the GPU is arguably the most parallel and most powerful processor in
a typical PC, it is certainly not the only processor. e CPU, now multicore and
application
programming interface
(API)
A s e t o f f u n c t i o n
and data structure
de nitions providing an
interface to a library of
functions.
B.1 Introduction B-5
soon to be manycore, is a complementary, primarily serial processor companion
to the massively parallel manycore GPU. Together, these two types of processors
comprise a heterogeneous multiprocessor system.
e best performance for many applications comes from using both the CPU
and the GPU. is appendix will help you understand how and when to best split
the work between these two increasingly parallel processors.
GPU Evolves into Scalable Parallel Processor
GPUs have evolved functionally from hardwired, limited capability VGA controllers
to programmable parallel processors. is evolution has proceeded by changing
the logical (API-based) graphics pipeline to incorporate programmable elements
and also by making the underlying hardware pipeline stages less specialized and
more programmable. Eventually, it made sense to merge disparate programmable
pipeline elements into one uni ed array of many programmable processors.
In the GeForce 8-series generation of GPUs, the geometry, vertex, and pixel
processing all run on the same type of processor. is uni cation allows for
dramatic scalability. More programmable processor cores increase the total system
throughput. Unifying the processors also delivers very e ective load balancing,
since any processing function can use the whole processor array. At the other end
of the spectrum, a processor array can now be built with very few processors, since
all of the functions can be run on the same processors.
Why CUDA and GPU Computing?
is uniform and scalable array of processors invites a new model of programming
for the GPU. e large amount of oating-point processing power in the GPU
processor array is very attractive for solving nongraphics problems. Given the large
degree of parallelism and the range of scalability of the processor array for graphics
applications, the programming model for more general computing must express
the massive parallelism directly, but allow for scalable execution.
GPU computing is the term coined for using the GPU for computing via a
parallel programming language and API, without using the traditional graphics
API and graphics pipeline model. is is in contrast to the earlier
General Purpose
computation on GPU (GPGPU)
approach, which involves programming the GPU
using a graphics API and graphics pipeline to perform nongraphics tasks.
Compute Unifed Device Architecture (CUDA) is a scalable parallel programming
model and so ware platform for the GPU and other parallel processors that allows
the programmer to bypass the graphics API and graphics interfaces of the GPU
and simply program in C or C++. e CUDA programming model has an SPMD
(single-program multiple data) so ware style, in which a programmer writes a
program for one thread that is instanced and executed by many threads in parallel
on the multiple processors of the GPU. In fact, CUDA also provides a facility for
programming multiple CPU cores as well, so CUDA is an environment for writing
parallel programs for the entire heterogeneous computer system.
GPU computing U s i n g
a GPU for computing via
a parallel programming
language and API.
GPGPU Using a GPU
for general-purpose
computation via a
traditional graphics API
and graphics pipeline.
CUDA A s c a l a b l e
parallel programming
model and language based
on C/C++. It is a parallel
programming platform
for GPUs and multicore
CPUs.
B-6 Appendix B Graphics and Computing GPUs
GPU Unifes Graphics and Computing
With the addition of CUDA and GPU computing to the capabilities of the GPU,
it is now possible to use the GPU as both a graphics processor and a computing
processor at the same time, and to combine these uses in visual computing
applications. e underlying processor architecture of the GPU is exposed in two
ways: rst, as implementing the programmable graphics APIs, and second, as a
massively parallel processor array programmable in C/C++ with CUDA.
Although the underlying processors of the GPU are uni ed, it is not necessary
that all of the SPMD thread programs are the same. e GPU can run graphics
shader programs for the graphics aspect of the GPU, processing geometry, vertices,
and pixels, and also run thread programs in CUDA.
e GPU is truly a versatile multiprocessor architecture, supporting a variety of
processing tasks. GPUs are excellent at graphics and visual computing as they were
speci cally designed for these applications. GPUs are also excellent at many general-
purpose throughput applications that are “ rst cousins” of graphics, in that they
perform a lot of parallel work, as well as having a lot of regular problem structure.
In general, they are a good match to data-parallel problems (see Chapter 6 ),
particularly large problems, but less so for less regular, smaller problems.
GPU Visual Computing Applications
Visual computing includes the traditional types of graphics applications plus many
new applications. e original purview of a GPU was “anything with pixels,” but it
now includes many problems without pixels but with regular computation and/or
data structure. GPUs are e ective at 2D and 3D graphics, since that is the purpose
for which they are designed. Failure to deliver this application performance would
be fatal. 2D and 3D graphics use the GPU in its “graphics mode,” accessing the
processing power of the GPU through the graphics APIs, OpenGL™, and DirectX™.
Games are built on the 3D graphics processing capability.
Beyond 2D and 3D graphics, image processing and video are important
applications for GPUs. ese can be implemented using the graphics APIs or as
computational programs, using CUDA to program the GPU in computing mode.
Using CUDA, image processing is simply another data-parallel array program. To
the extent that the data access is regular and there is good locality, the program
will be e cient. In practice, image processing is a very good application for GPUs.
Video processing, especially encode and decode (compression and decompression
according to some standard algorithms), is quite e cient.
e greatest opportunity for visual computing applications on GPUs is to “break
the graphics pipeline.” Early GPUs implemented only speci c graphics APIs, albeit at
very high performance. is was wonderful if the API supported the operations that
you wanted to do. If not, the GPU could not accelerate your task, because early GPU
functionality was immutable. Now, with the advent of GPU computing and CUDA,
these GPUs can be programmed to implement a di erent virtual pipeline by simply
writing a CUDA program to describe the computation and data ow that is desired. So,
all applications are now possible, which will stimulate new visual computing approaches.
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