Embedded Multiprocessors: Scheduling and Synchronization, Second Edition

384 Pages·2009·2.375 MB·English

by  Sundararajan Sriram, Shuvra S. Bhattacharyya

SECOND EDITION
EMBEDDED
MULTIPROCESSORS
Scheduling and Synchronization
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Signal Processing and Communications
EDITORIAL BOARD
Maurice G. Ballanger, Conservatoire National
des Arts et Métiers (CNAM), Paris
Ezio Biglieri, Politecnico di Torino, Italy
Sadaoki Furui, Tokyo Institute of Technology
Yih-Fang Huang, University of Notre Dame
Nikil Jayant, Georgia Institute of Technology
Aggelos K. Katsaggelos, Northwestern University
Mos Kaveh, University of Minnesota
P. K. Raja Rajasekaran, Texas Instruments
John Aasted Sorenson, IT University of Copenhagen
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S E C O N D E D I T I O N
EMBEDDED
MULTIPROCESSORS
Scheduling and Synchronization
Sundararajan Sriram
Shuvra S. Bhattacharyya
Boca Raton London New York
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Library of Congress Cataloging‑in‑Publication Data
Sriram, Sundararajan, 1968‑
Embedded multiprocessors : scheduling and synchronization / authors,
Sundararajan Sriram, Shuvra S. Bhattacharyya. ‑‑ 2nd ed.
p. cm.
“A CRC title.”
Includes bibliographical references and index.
ISBN 978‑1‑4200‑4801‑8 (hardcover : alk. paper)
1. Embedded computer systems. 2. Multiprocessors. 3. Multimedia systems. 4.
Scheduling. 5. Memory management (Computer science) I. Bhattacharyya, Shuvra S.,
1968‑ II. Title.
TK7895.E42S65 2009
004.16‑‑dc22 2008050949
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v
To my parents, and Uma
Sundararajan Sriram
To Arundhati
Shuvra S. Bhattacharyya

vii
FOREWORD
Embedded systems are computers that are not first and foremost comput-
ers. They are pervasive, appearing in automobiles, telephones, pagers, consumer
electronics, toys, aircraft, trains, security systems, weapons systems, printers,
modems, copiers, thermostats, manufacturing systems, appliances, etc. A techni-
cally active person today probably interacts regularly with more embedded sys-
tems than conventional computers. This is a relatively recent phenomenon. Not
so long ago automobiles depended on finely tuned mechanical systems for the
timing of ignition and its synchronization with other actions. It was not so long
ago that modems were finely tuned analog circuits.
Embedded systems usually encapsulate domain expertise. Even small soft-
ware programs may be very sophisticated, requiring deep understanding of the
domain and of supporting technologies such as signal processing. Because of
this, such systems are often designed by engineers who are classically trained in
the domain, for example, in internal combustion engines or in communication
theory. They have little background in the theory of computation, parallel com-
puting, and concurrency theory. Yet they face one of the most difficult problems
addressed by these disciplines, that of coordinating multiple concurrent activities
in real time, often in a safety-critical environment. Moreover, they face these
problems in a context that is often extremely cost-sensitive, mandating optimal
designs, and time-critical, mandating rapid designs.
Embedded software is unique in that parallelism is routine. Most modems
and cellular telephones, for example, incorporate multiple programmable proces-
sors. Moreover, embedded systems typically include custom digital and analog
hardware that must interact with the software, usually in real time. That hardware
operates in parallel with the processor that runs the software, and the software
must interact with it much as it would interact with another software process run-
ning in parallel. Thus, in having to deal with real-time issues and parallelism, the
designers of embedded software face on a daily basis problems that occur only in
esoteric research in the broader field of computer science.
viii EMBEDDED MULTIPROCESSORS
Computer scientists refer to use of physically distinct computational
resources (processors) as “parallelism,” and to the logical property that multiple
activities occur at the same time as “concurrency.” Parallelism implies concur-
rency, but the reverse is not true. Almost all operating systems deal with concur-
rency, which is managed by multiplexing multiple processes or threads on a
single processor. A few also deal with parallelism, for example by mapping
threads onto physically distinct processors. Typical embedded systems exhibit
both concurrency and parallelism, but their context is different from that of gen-
eral-purpose operating systems in many ways.
In embedded systems, concurrent tasks are often statically defined, largely
unchanging during the lifetime of the system. A cellular phone, for example, has
relatively few distinct modes of operation (dialing, talking, standby, etc.), and in
each mode of operation, a well-defined set of tasks is concurrently active (speech
coding, radio modem, dual-tone multi-frequency encoding, etc.). The static struc-
ture of the concurrency affords opportunities for much more detailed analysis and
optimization than would be possible in a more dynamic environment. This book
is about such analysis and optimization.
The ordered transaction strategy, for example, leverages that relatively
static nature of embedded software to dramatically reduce the synchronization
overhead of communication between processors. It recognizes that embedded
software is intrinsically less predictable than hardware and more predictable than
general-purpose software. Indeed, minimizing synchronization overhead by
leveraging static information about the application is the major theme of this
book.
In general-purpose computation, communication is relatively expensive.
Consider for example the interface between the audio hardware and the software
of a typical personal computer today. Because the transaction costs are extremely
high, data is extensively buffered, resulting in extremely long latencies. A path
from the microphone of a PC into the software and back out to the speaker typi-
cally has latencies of hundreds of milliseconds. This severely limits the utility of
the audio hardware of the computer. Embedded systems cannot tolerate such
latencies.
A major theme of this book is communication between components. The
methods given in the book are firmly rooted in a manipulable and tractable for-
malism, and yet are directly applied to hardware design. The closely related IPC
(interprocessor communication) graph and synchronization graph models, intro-
duced in Chapters 7 and 9, capture the essential properties of this communica-
tion. By use of graph-theoretic properties of IPC and synchronization graphs,
hello
FOREWORD ix
optimization problems are formulated and solved. For example, the notion of
resynchronization, where explicit synchronization operations are minimized
through manipulation of the synchronization graph, proves to be an effective
optimization tool.
In some ways, embedded software has more in common with hardware
than with traditional software. Hardware is highly parallel. Conceptually, hard-
ware is an assemblage of components that operate continuously or discretely in
time and interact via synchronous or asynchronous communication. Software is
an assemblage of components that trade off use of a CPU, operating sequentially,
and communicating by leaving traces of their (past and completed) execution on
a stack or in memory.
Hardware is temporal. In the extreme case, analog hardware operates in a
continuum, a computational medium that is totally beyond the reach of software.
Communication is not just synchronous; it is physical and fluid. Software is
sequential and discrete. Concurrency in software is about reconciling sequences.
Concurrency in hardware is about reconciling signals. This book examines paral-
lel software from the perspective of signals, and identifies joint hardware/soft-
ware designs that are particularly well-suited for embedded systems.
The primary abstraction mechanism in software is the procedure (or the
method in object-oriented designs). Procedures are terminating computations.
The primary abstraction mechanism in hardware is a module that operates in par-
allel with the other components. These modules represent nonterminating com-
putations. These are very different abstraction mechanisms. Hardware modules
do not start, execute, complete, and return. They just are. In embedded systems,
software components often have the same property. They do not terminate.
Conceptually, the distinction between hardware and software, from the
perspective of computation, has only to do with the degree of concurrency and
the role of time. An application with a large amount of concurrency and a heavy
temporal content might as well be thought of using the abstractions that have
been successful for hardware, regardless of how it is implemented. An applica-
tion that is sequential and ignores time might as well be thought of using the
abstractions that have succeeded for software, regardless of how it is imple-
mented. The key problem becomes one of identifying the appropriate abstrac-
tions for representing the design. This book identifies abstractions that work well
for the joint design of embedded software and the hardware on which it runs.
The intellectual content in this book is high. While some of the methods it
describes are relatively simple, most are quite sophisticated. Yet examples are
given that concretely demonstrate how these concepts can be applied in practical
hardware architectures. Moreover, there is very little overlap with other books on
parallel processing. The focus on application-specific processors and their use in
embedded systems leads to a rather different set of techniques. I believe that this