Article about graphics processors by David Geer

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Computer

I N D U S T R Y T R E N D S

Published by the IEEE Computer Society

or several years, graphics pro-

cessing units’ performance has

been increasing faster than the

pace predicted by Moore’s law.

This has occurred because

GPUs must meet the demands of

increasingly complex visual effects in

games and entertainment applications.

“Instead of doubling every 18

months, GPU performance has been

increasing fivefold every 18 months.

This is equivalent to doubling in just

under every eight months,” noted Ian

Buck, software architect with Nvidia,

a vendor of graphics and digital-media

processors. Figure 1 illustrates this

trend.

Their performance and functionality

have made GPUs potentially attractive

as coprocessors for general-purpose

computation.

In recognition of this, major graph-

ics chip manufacturers such as Nvidia

and ATI Technologies have added sup-

port for floating-point computation

and released compilers for high-level

languages.

Researchers have also developed

new algorithms and applications that

exploit GPUs’ parallelism and vector-

processing capabilities, in which one

operation can process an entire vector

of numbers.

Users and researchers are thus

increasingly working with general-pur-

pose GPUs (GPGPUs) in areas other

than gaming and entertainment, such

as geometric, scientific, and database

computations; medical imaging; and

computer vision.

However, their complex program-

ming environment and other chal-

lenges could affect the processors’

ultimate popularity.

ABOUT THE GPU

GPUs typically are used in game

consoles or as graphics coprocessors to

CPUs, mainly for rendering geometric

primitives such as polygons.

Researchers have studied the use of

graphics hardware for general-purpose

computation since the late 1970s, a

process that accelerated with GPUs’

wider deployment during the past few

years.

Several factors have made GPUs

more useful for some types of general-

purpose computation. For example,

vendors have added hardware to sup-

port branching, which lets programs

alter their instructions based on results

from previous instructions, said Nvidia

software engineer Mark Harris.

This enables high-level language con-

structs like if-then-else statements—

which let a system conditionally

execute a group of statements depend-

ing on an expression’s value—and

while loops—which let a system repeat-

edly execute code based on a given

Boolean condition. Both are useful for

general-purpose computation, Harris

explained.

Also, GPUs are widely available,

commodity products that typically cost

only about $500, noted Tim Purcell,

graphics architect with Nvidia’s

Graphics Architecture Group. As for

CPUs, said Jon Peddie, president of Jon

Peddie Research, “A high-end Itanium

can cost as much as $1,000.”

High performance

GPUs frequently have slower clock

speeds than premium CPUs, but

because graphics chips handle work in

parallel, they can offer more perfor-

mance.

The G70, Nvidia’s most recent GPU,

performs up to 165 gigaflops. Intel

declined to release the performance of

its high-end CPUs. However, said

University of Virginia assistant profes-

sor David Luebke, a 3-GHz, dual-core

Intel Pentium 4 Extreme Edition’s arith-

metic units will theoretically run as

much as 24.6 Gflops. Although Intel’s

fastest chip would offer somewhat more

performance, it would still be consider-

ably less than that of a high-end GPU.

“Intense competition between

Nvidia and ATI Technologies has dri-

ven GPU speeds higher with each new

processor release. And the competition

by chip makers to have game-console

makers use their products has intensi-

fied this process,” said Tom Halfhill,

senior analyst with In-Stat, a market

research firm.

GPUs are highly parallel streaming

processors optimized for vector oper-

ations. Streaming processors present

data in a fixed order to processing units

with limited memory, explained Suresh

Venkatasubramanian, technical staff

member of AT&T Labs’ Information

Visualization Research Group. Each

unit performs a fixed set of operations

on each data item and passes it on, he

said.

A GPU’s multiple-instruction, mul-

Taking the Graphics

Processor beyond

Graphics

David Geer

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dards over a much faster serial com-

munications system.

GPGPU USES

GPUs rely on the high arithmetic

intensity necessary to process graphics,

noted Luebke. Thus, he explained,

applications that involve numeric com-

putations on large grids of data are

well suited to GPGPUs.

This includes linear algebra and the

simulation of complex physical pro-

cesses, said Arie Kaufman, chair of

Stonybrook University’s Computer

Science Department.

Other examples are differential

equation solvers and scientific compu-

tations, such as the fast Fourier trans-

forms used in real-time MPEG video

compression and audio rendering,

Luebke noted.

“Signal processing operations are

usually computationally intensive and

data parallel. GPUs’ arithmetic capa-

bilities are suitable for them,” Kaufman

said.

Other suitable applications involve

fluid dynamics, including climate mod-

eling, weather prediction, and oceanic

and atmospheric studies; and molecu-

lar dynamics, including protein and

biomolecular simulations, chemical

reactions, and material sciences.

GPGPUs also work well on geomet-

ric computations such as Voronoi dia-

tiple-data (MIMD) pipelines perform

vertex processing, which helps render

specific points in 3D scenes based on

their coordinates. Single-instruction,

multiple-data (SIMD) pipelines pro-

duce colors and 3D effects for each

pixel, the smallest unit of an image dis-

played on a screen.

Together, the two types of parallel-

processing pipelines offer more per-

formance than CPUs.

Strict pipelining, in which systems

efficiently process all data items in

pipeline order, enables GPUs to easily

handle data without extensive caching.

“CPUs need caching because their

programs are far more general, and the

sequence of memory accesses is far less

predictable than with the GPU, for

which the program is explicitly con-

strained,” noted Venkatasubramanian.

Reducing the number of on-chip

caches leaves GPUs with more room

for additional computational units,

noted Nvidia’s Purcell.

Programmability

Nvidia and ATI have made their

commodity GPUs programmable so

that the processors can be used more

flexibly, such as for general-purpose

computation.

Because GPUs are now programma-

ble, chip makers have developed com-

pilers that translate commonly used

high-level languages such as C and

C++ into the less-familiar languages,

such as Cg (C for graphics), that the

processors run, noted University of

North Carolina professor Ming Lin.

32-bit floating-point capabilities

According to Purcell, his company

and ATI recently added floating-point

arithmetic logic units to their GPUs.

This provides support for floating-

point computation, critical for preci-

sion in both graphics and general-

purpose applications.

Early GPUs could offer only eight-

bit color. The eight bits of code avail-

able for each color limited a color’s

dynamic range to only 256 levels, said

Lin.

Current Nvidia and ATI GPUs sup-

port 16-bit floating-point color directly

in hardware. Nvidia’s and ATI’s chips

support 32-bit and 24-bit color, respec-

tively, via additional programming,

noted research assistant professor

Naga Govindaraju of the University of

North Carolina.

Introducing 32-bit floating-point

capabilities added precision and the

ability to perform more complex com-

putations and thus made GPUs better

able to handle general-purpose func-

tions, explained Lin.

Memory bandwidth

The latest GPU architectures provide

considerable memory bandwidth,

allowing faster off-chip and on-chip

data access and thereby increasing per-

formance, said the University of

Virginia’s Luebke.

“Peak memory bandwidth is now

38.4 Gbytes per second on the Nvidia

7800 GTX,” according to Govindaraju.

A high-end CPU has a peak memory

bandwidth of only 6.4 Gbps, he noted.

Chip makers have achieved high

memory bandwidth in several ways.

For example, new GPUs accelerate off-

chip memory communications by

using the PCI Express bus system,

which implements existing peripheral-

component-interconnect programming

concepts and communications stan-

September 2005

Jan

Jun

Jan

Apr

Jan

May

100

150

200

2003

2004

2005

Performance (Gflops)

ATI R420

Moore’s law

3.0 GHz dual-core

Pentium 4

Nvidia G70

Source: Nvidia

Figure 1. The maximum number of gigaflops produced by two leading graphics processing

units, Nvidia’s G70 and ATI Technologies’ R420, show how GPU performance has improved

faster than that called for by Moore’s law and that of Intel’s Pentium 4.

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Computer

for general-purpose programming on

GPUs,” explained the University of

North Carolina’s Lin.

Also, GPUs’ strictly parallel opera-

tions tightly constrain their program-

ming environment. This is a particular

challenge for programmers used to

working with scalar or sequential

applications.

According to Nvidia’s Harris, pro-

grammers must spend time and effort

learning to work with APIs, such as

OpenGL, designed specifically for

computer graphics, whose core con-

cepts are different than APIs typically

used in general-purpose computation.

There are also few debuggers or pro-

filers—which track an application’s

performance by collecting and check-

ing information during code execu-

tion—for use in programming

GPGPUs, although this is beginning

to change, observed AT&T Labs’

Venkatasubramanian.

Purcell noted that GPU’s can’t cur-

rently perform arbitrary memory

writes.

Vendors have kept some architec-

tural details secret for competitive rea-

sons, Purcell noted. In some cases, this

has kept researchers from having

access to information that could help

grams, distance computations, and

robot motion planning and collision

detection.

In addition, GPUs’ high memory

bandwidth and parallel processing can

accelerate complex database opera-

tions including aggregates, predicates,

Boolean combinations, selection

queries, and data mining, explained

Kaufman.

GPGPUs have cracked encryption

used for passwords and other pur-

poses. The processors also are effective

for nontraditional graphics-related

purposes such as medical imaging, ray

tracing, photon mapping, and subsur-

face scattering.

NOT READY FOR PRIME TIME

GPUs need more work before they

are ready for more general-purpose

uses. For example, noted Nvidia’s

Purcell, “GPUs aren’t good at all types

of general computation. They are

highly parallel and thus generally

aren’t good at executing code that is

inherently serial.”

GPUs are designed to process graph-

ics and thus are more difficult to pro-

gram for general-purpose computation

than CPUs. “In addition, there are few

programming tools and little support

explore new general-purpose uses for

GPUs.

ccording to the University of

Virginia’s Luebke, “The driving

market for GPUs is the video

game industry, and the needs of that

industry dominate the designs and

roadmaps of vendors.” However,

industry observers say, the processors’

changing design will also make them

more useful for many types of general-

purpose computation.

Jon Peddie said that during the next

few years, GPGPUs will expand from

24 to 32 pipelines, and each pipeline

will include more floating-point

processors and larger cache memories.

“You’ll also continue to see the

speed increases that we’ve come to

expect from GPUs,” said Nvidia’s

Purcell. He predicted that as games

start to integrate general-computing

techniques, such as those used in

physics applications, developers will

create better programming models not

tied to graphics APIs.

Meanwhile, Purcell said, GPU clus-

ters might prove useful for high-per-

formance general-purpose computing

if the problems they work on are suffi-

ciently parallelizable.

“The rapid growth rate and high-

performance capabilities of GPUs are

very promising for conducting GPGPU

research,” said the University of North

Carolina’s Govindaraju. “The chal-

lenge, however, will be redesigning tra-

ditional CPU-based algorithms to

efficiently exploit the computational

power of GPUs.” I

David Geer is a freelance technology

journalist based in Ashtabula, Ohio. Con-

tact him at geercom@alltel.net.

I n d u s t r y T r e n d s

Editor: Lee Garber, Computer,

l.garber@computer.org

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