Dan's Research Page
Nov 21, 2007 Updated
Literature Review Survey
Nov 7, 2007
Literature Review Survey
Topic: Parallel Computing:
Description: Parallel computing
is the partitioning of code to be executed on multiple processors. The research will cover two main areas. The first will be how the code split into
pieces to be executed by different processors.
The second part will be how an implicit compiler decides how to split up
the code to be executed. The problem
then becomes how to optimize these compilers so that they divide up code most
Motivation: There has been for a long time projects that
give people the opportunity to put their own computer to work in conjunction
with others to problem solve. The idea
of a network that splits up a problem to solve it quicker has always been
fascinating. If perfected in the up
coming years, there could be a network of computers that are able to solve
problems that previously were unimaginable.
 B. Barney,
"Introduction to Parallel Computing," Introduction to Parallel Computing, Livermore Computing, June
2007. [Online]. Available: http://www.llnl.gov/computing/tutorials/parallel_comp.
[Accessed: Sept. 11, 2007].[This website gives a large overview to
parallel computing including terminology, concepts, and other models. Parallel computing is using multiple
processors simultaneously to solve a problem.
Parallel computing saves time, it can prove to be more cost efficient
and it can be used to solve larger problems.
It talks about the difference between automatic and manual parallelism
dealing with compilers. A fully
automatic or implicit compiler is able to find areas in the code which can be
split and sent to other processors.
Automatic compilers are even able to analyze the cost it would take to
execute the code through parallelism and see if it is worth splitting. The paper also discusses different parallel
models for programming and their implementations. Overall, this website is an excellent guide
to parallel computing.]
Emmons, "Multiprocessor Optimizations: Fine-Tuning Concurrent Access to
Large Data Collections," MSDN Magazine, 2007. [Online]. Available: http://msdn.microsoft.com/msdnmag/issues/01/08/concur/default.aspx.
[Accessed: Sept. 11, 2007].
[This shows the need for concurrent
access of information in a program and how a high concurrency machine will out
perform a high end multiple cpu machine with low concurrency.]
 J. Plazek, J. Kitowski, and K. Banas,
"Efficiency Comparison of Explicit and Implicit Parallel Programming for a
FEM Problem on HP Exemplar Systems," Cracow
University of Technology, 1997.
[Online]. Available: http://www.icsr.agh.edu.pl/publications/html/hiper97_kb/hiper97_kb.html.
[Accessed: Sept. 11, 2007].[Compares implicit and explicit parallelism
for solving finite element problems.]
 N. Drakos, “Implicit Parallelism,”
Computer Based Learning Unit, University
of Leeds, 1996. [Online].
[Accessed: Sept. 17, 2007].
[Here, the author discusses the idea of
implicit parallelism. As noted, the
parallelism is separate from the programmer.
Project FX and Paradigm are introduced as implicit languages or
languages that determine parallelism through the compiler. Declarative languages are also discussed such
as Functional and Logic which are very high level languages that have opened
hope for stronger implicit parallelism. Problems such as the compilers inability
to comprehend the size of components of code or how the system is not fully
efficient are introduced to think about.]
 S. Chang, “Parallelization of Codes on
the SGI Origins,” NASA Ames
[Online]. Available: http://people.nas.nasa.gov/~schang/origin_parallel.html#fraction
[Accessed: Sept 26, 2007]. [This website is another great reference
tool for parallel computing. Again, the
main goal of parallel computing is to cut down on the time required to execute
code. The website offers insight as well
as basic theory behind parallel computing.
It also stresses other components involved in parallel execution of code
and efficiency of execution. There are
even other tools and theories on the site that will allow a programmer to
determine the performance of parallel code and how to analyze the code.]
 D Leijen and J Hall, “Optimize Managed
Code For Multi-Core Machines,” The Microsoft Journal for Developers, 2007.
[Online]. Available: http://msdn.microsoft.com/msdnmag/issues/07/10/Futures/default.aspx
[Accessed: Sept 26, 2007]. [As stated, single processor speeds are
coming to a halt and the need for multi-processor computing is clear. The Task Parallel Library is one tool that
programmers have to write new code that will execute better on multiple
processors. Although the methods here
are more directed towards explicit parallelism, a strong need for parallelism
is apparent as technology heads towards multiple processor systems. Examples including a 3D rendering are
available as well as comparisons of threading vs. TPL tools for parallelism.]
“Optimization Topics,” Maui High Performance Computing
Center, 2004. [Online.] Available: http://www.mhpcc.edu/training/workshop2/optimization/MAIN.html#OptimizationTypes
[Accessed: Oct. 1, 2007]. [Given on this website is a basic
understanding of optimization. The
process of optimizing code is given by the author in a seven step
source code and verify program correctness (usually with optimization switches
your code; identify opportunities for performance improvement.
hand-tuning operations, such as algorithm optimization, re-coding of
preprocessor optimizations and/or compile with optimization switches on
your code; Examine blocks of code that consume the most execution time.
apply various optimizations to such blocks.
mathematical correctness of the program.
Also included are compiler optimization descriptions and a basic
overview of compiler optimizations.]