A Brief Overview of Parallel Algorithms
Guy E. Blelloch and Bruce M. Maggs
Carnegie Mellon University
This is a draft of a paper that will appear in ACM's Computing Surveys
in the 50th-aniversary issue, and is a condensed version of a chapter
that will appear in the CRC Handbook on Computer Science.
As more computers have incorporated some form of parallelism, the
emphasis in algorithm design has shifted from sequential algorithms to
parallel algorithms, i.e., algorithms in which multiple operations are
performed simultaneously. As a consequence, our understanding of
parallel algorithms has increased remarkably over the past ten years.
The most important developments in the field have occurred in three
broad areas: parallel models of computation, parallel algorithmic
techniques, and parallel complexity theory. This chapter surveys
these three areas. So many parallel algorithms have now been designed
that a chapter of this length cannot cover even a small fraction of
them. Hence, this chapter does not discuss individual algorithms in
any detail. The interested reader should consult the following texts:
[2, 3, 4, 5, 6, 7].
Developing a standard parallel model of computation for analyzing
algorithms has proven difficult because different parallel computers
tend to vary significantly in their organizations. In spite of this
difficulty, useful parallel models have emerged, along with a deeper
understanding of the modeling process. In this section we describe
three important principles that have emerged.
Work-efficiency. In designing a parallel algorithm, it is more
important to make it efficient than to make it asymptotically fast.
The efficiency of an algorithm is determined by the total number of
operations, or work that it performs. On a sequential
machine, an algorithm's work is the same as its time. On a parallel
machine, the work is simply the processor-time product. Hence, an
algorithm that takes time t on a P-processor machine performs work
W = Pt. In either case, the work roughly captures the actual cost to
perform the computation, assuming that the cost of a parallel machine
is proportional to the number of processors in the machine. We call
an algorithm work-efficient (or just efficient) if it performs
the same amount of work, to within a constant factor, as the fastest
known sequential algorithm. For example, a parallel algorithm that
sorts n keys in time using
processors is efficient since the work, , is as good as
any (comparison-based) sequential algorithm. However, a sorting
algorithm that runs in time using processors is not
efficient. The first algorithm is better than the second - even
though it is slower - because it's work, or cost, is smaller. Of
course, given two parallel algorithms that perform the same amount of
work, the faster one is generally better.
The notion of work-efficiency leads to another important observation:
a model can be useful without mimicking any real or even realizable
machine. Instead, it suffices that any algorithm that runs
efficiently in the model can be translated into an algorithm that runs
efficiently on real machines. As an example, consider the widely-used
parallel random-access machine (PRAM) model. In the PRAM model, a set
of processors share a single memory system. In a single unit of time,
each processor can perform an arithmetic, logical, or memory access
operation. This model has often been criticized as unrealistically
powerful, primarily because no shared memory system can perform memory
accesses as fast as processors can execute local arithmetic and
The important observation, however, is that for a model
to be useful we only require that algorithms that are efficient
in the model can be mapped to algorithms that are efficient on
realistic machines, not that the model is realistic.
In particular, any algorithm that runs efficiently in a
P-processor PRAM model can be translated into an algorithm that runs
efficiently on a -processor machine with a latency L memory
system, a much more realistic machine.
In the translated algorithm, each of the processors emulates L
PRAM processors. The latency is ``hidden'' because a processor has
useful work to perform while waiting for a memory access to complete.
Although the translated algorithm is a factor of L slower than the
PRAM algorithm, it uses a factor of L fewer processors, and hence is
Modeling Communication. To get the best performance out of a
parallel machine, it is often helpful to model the communication
capabilities of the machine, such as its latency, explicitly. The
most important measure is the communication bandwidth. The bandwidth
available to a processor is the maximum rate at which it can
communicate with other processors or the memory system. Because it is
more difficult to hide insufficient bandwidth than large latency, some
measure of bandwidth is often included in parallel models. Sometimes
the specific topology of the communication network is modeled as well.
Although including this level of detail in the model often complicates
the design of parallel algorithms, it's essential for designing the
low-level communication primitives for the machine. In addition to
modeling basic communication primitives, other operations supported by
hardware, including synchronization and concurrent memory accesses,
are often modeled, as well as operations that mix computation and
communication, such as fetch-and-add and scans. A final consideration
is whether the machine supports shared memory, or whether all
communication relies on passing messages between the processors.
A major advance in parallel algorithms has been the identification of
fundamental algorithmic techniques. Some of these techniques are also
used by sequential algorithms, but play a more prominent role in
parallel algorithms, while others are unique to parallelism. Here we
list some of these techniques with a brief description of each.
Divide-and-Conquer. Divide-and-conquer is a natural paradigm
for parallel algorithms. After dividing a problem into two or more
subproblems, the subproblems can be solved in parallel. Typically the
subproblems are solved recursively and thus the next divide step
yields even more subproblems to be solved in parallel. For example,
suppose we want to compute the convex-hull of a set of n points in
the plane (i.e., compute the smallest convex polygon that encloses all
of the points). This can be implemented by splitting the points into
the leftmost and rightmost , recursively finding the convex
hull of each set in parallel, and then merging the two resulting
hulls. Divide-and-conquer has proven to be one of the most powerful
techniques for solving problems in parallel with applications ranging
from linear systems to computer graphics and from factoring large
numbers to n-body simulations.
Randomization. The use of random numbers is ubiquitous in
parallel algorithms. Intuitively, randomness is helpful because it
allows processors to make local decisions which, with high
probability, add up to good global decisions. For example, suppose we
want to sort a collection of integer keys. This can be accomplished
by partitioning the keys into buckets then sorting within each bucket.
For this to work well, the buckets must represent non-overlapping
intervals of integer values, and contain approximately the same number
of keys. Randomization is used to determine the boundaries of the
intervals. First each processor selects a random sample of its
keys. Next all of the selected keys are sorted together. Finally
these keys are used as the boundaries. Such random sampling is also
used in many parallel computational geometry, graph, and string
matching algorithms. Other uses of randomization include symmetry
breaking, load balancing, and routing algorithms.
Parallel Pointer Manipulations. Many of the traditional
sequential techniques for manipulating lists, trees, and graphs do not
translate easily into parallel techniques. For example techniques
such as traversing the elements of a linked list, visiting the nodes
of a tree in postorder, or performing a depth-first traversal of a
graph appear to be inherently sequential. Fortunately, each of these
techniques can be replaced by efficient parallel techniques. These
parallel techniques include pointer jumping, the Euler-tour technique,
ear decomposition, and graph contraction. For example, one way to
label each node of an n-node list (or tree) with the label of the
last node (or root) is to use pointer jumping. In each
pointer-jumping step each node in parallel replaces its pointer with
that of its successor (or parent). After at most steps,
every node points to the same node, the end of the list (or root of
Others. Other useful techniques include finding small graph
separators for partitioning data among processors to reduce
communication, hashing for balancing load across processors and
mapping addresses to memory, and iterative techniques as a replacement
for direct methods for solving linear systems.
These techniques have led to efficient parallel algorithms in most
problem areas for which efficient sequential algorithms are known.
In fact, some of the techniques originally developed for parallel
algorithms have led to improvements in sequential algorithms.
Researchers have developed a theory of the parallel complexity of
computational problems analogous to the theory of NP-completeness. A
problem is said to belong to the class NC (Nick's Class) if it
can be solved in time polylogarithmic in the size of the problem using
at most a polynomial number of processors. The class NC in parallel
complexity theory plays the role of P in sequential complexity,
i.e., the problems in NC are thought to be tractable in parallel.
Examples of problems in NC include sorting, finding minimum-cost
spanning trees, and finding convex hulls. A problem is said to be
P-complete if it can be solved in polynomial time and if its
inclusion in NC would imply that NC = P. Hence, the notion of
P-completeness plays the role of NP-completeness in sequential
complexity. (And few believe that NC = P.) Examples of
P-complete problems include finding a maximum flow and finding a
lexicographically minimum independent set of nodes in a graph. Much
early work in parallel algorithms aimed at showing that certain
problems belonged to the class NC (without considering the issue of
efficiency). This work tapered off, however, as the importance of
work-efficiency became evident. Also, even if a problem is
P-complete, there may be efficient (but not necessarily
polylogarithmic time) parallel algorithms for solving it. For
example, several efficient and highly parallel algorithms are known
for solving the maximum flow problem, which is P-complete.
Recently the emphasis of research on parallel algorithms has shifted
to pragmatic issues. The theoretical work on algorithms has been
complemented by extensive experimentation. This experimental work has
yielded insights into how to build parallel machines ,
how to make parallel algorithms perform well in practice
, how to model parallel machines more accurately,
and how to express parallel algorithms in parallel programming
Two effective parallel programming paradigms have emerged:
control-parallel programming and data-parallel programming. In a
control-parallel program, multiple independent processes or functions
may execute simultaneously on different processors and communicate
with each other. Some of the most successful control-parallel
programming systems include Linda, MPI, and PVM. In each step of a
data-parallel program an operation is performed in parallel across a
set of data. Successful data-parallel programming languages include
*Lisp, NESL, and HPF. Although the data-parallel programming paradigm
might appear to be less general than the control-parallel paradigm,
most parallel algorithms found in the literature can be expressed more
naturally using data-parallel constructs.
There has also been a focus on solving problems from applied domains,
including computational biology, astronomy, seismology, fluid
dynamics, scientific visualization, computer-aided design, and
database management. Interesting algorithmic problems arising from
these domains include generating meshes for finite element analysis,
solving sparse linear systems, solving n-body problems, pattern
matching, ray tracing, and many others.
Commodity personal computers with multiple processors have begun to
appear on the market. As this trend continues, we expect the use of
parallel algorithms to increase dramatically.
George Almasi and Allan Gottlieb.
Highly Parallel Computing.
Benjamin/Cummings, Redwood City, CA, 1994.
Dimitri P. Bertsekas and John N. Tsitsiklis.
Parallel and Distributed Computation: Numerical
Prentice Hall, Englewood Cliffs, New Jersey, 1989.
An Introduction to Parallel Algorithms.
Addison Wesley, Reading, Mass., 1992.
Richard M. Karp and Vijaya Ramachandran.
Parallel algorithms for shared memory machines.
In J. Van Leeuwen, editor, Handbook of Theoretical Computer
Science--Volume A: Algorithms and Complexity. MIT Press, Cambridge, Mass.,
Vipin Kumar, Ananth Grama, Anshul Gupta and George Karypis.
Introduction to Parallel Computing: Design and Analysis of
Benjamin/Cummings, Redwood City, CA, 1994.
F. Thomson Leighton.
Introduction to Parallel Algorithms and Architectures: Arrays,
Trees, and Hypercubes.
Morgan Kaufmann, San Mateo, CA, 1992.
John H. Reif (editor).
Synthesis of Parallel Algorithms.
Morgan Kaufmann, San Mateo, California, 1993.
Gary W. Sabot (editor).
High Performance Computing: Problem Solving
with Parallel and Vector Architectures.
Addison Wesley, Reading, MA, 1995.
A Brief Overview of Parallel Algorithms
This document was generated using the LaTeX2HTML translator Version .95.3 (Nov 17 1995) Copyright © 1993, 1994, Nikos Drakos, Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html -split 0 short.tex.
The translation was initiated by Guy Blelloch on Tue Feb 27 10:00:02 EST 1996
- The latency of a memory system is the time from when a
processor makes a request to the memory system to when it receives the
Guy Blelloch, email@example.com