Despite five decades of research, parallel computing remains an
exotic, frontier technology on the fringes of mainstream computing.
Its much-heralded triumph over sequential computing has yet to
materialize. This is in spite of the fact that the processing needs
of many signal processing applications continue to eclipse the
capabilities of sequential computing. The culprit is largely the
software development environment. Fundamental shortcomings in the
development environment of many parallel computer architectures
thwart the adoption of parallel computing. Foremost, parallel
computing has no unifying model to accurately predict the execution
time of algorithms on parallel architectures. Cost and scarce
programming resources prohibit deploying multiple algorithms and
partitioning strategies in an attempt to find the fastest solution.
As a consequence, algorithm design is largely an intuitive art form
dominated by practitioners who specialize in a particular computer
architecture. This, coupled with the fact that parallel computer
architectures rarely last more than a couple of years, makes for a
complex and challenging design environment.
To navigate this environment, algorithm designers need a road
map, a detailed procedure they can use to efficiently develop high
performance, portable parallel algorithms. The focus of this book
is to draw such a road map. The Parallel Algorithm Synthesis
Procedure can be used to design reusable building blocks of
adaptable, scalable software modules from which high performance
signal processing applications can be constructed. The hallmark of
the procedure is a semi-systematic process for introducing
parameters to control the partitioning andscheduling of computation
and communication. This facilitates the tailoring of software
modules to exploit different configurations of multiple processors,
multiple floating-point units, and hierarchical memories. To
showcase the efficacy of this procedure, the book presents three
case studies requiring various degrees of optimization for parallel
execution.
This book can be used as a reference for algorithm designers or
as a text for an advanced course on parallel programming.
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