This document discusses parallel architecture and parallel programming. It begins with an introduction to von Neumann architecture and serial computation. Then it defines parallel architecture, outlines its benefits, and describes classifications of parallel processors including multiprocessor architectures. It also discusses parallel programming models, how to design parallel programs, and examples of parallel algorithms. Specific topics covered include shared memory and distributed memory architectures, message passing and data parallel programming models, domain and functional decomposition techniques, and a case study on developing parallel web applications using Java threads and mobile agents.

1. Parallel Architecture
&
Parallel Programming
Submitted:
Dr.Hesham El-Zouka
By:Eng. Ismail Fathalla El-Gayar

2. Content:-
• Introduction
– Von-Neumann Architecture.
– Serial ( Single ) Computational.
– Concepts and Terminology
• Parallel Architecture
– Definition
– Benefits & Advantages
– Distinguishing Parallel Processors
– Multiprocessor Architecture Classifications
– Parallel Computer Memory Architectures
• Parallel Programming
– Definition
– Parallel Programming Model
– Designing Parallel Programs
– Parallel Algorithm Examples
– Conclusion
• Case Study

3. Introduction:
• Von-Neumann Architecture
Since then, virtually all computers
have followed this basic design, which
Comprised of four main components:
– Memory
– Control Unit
– Arithmetic Logic Unit
– Input/output

4. Introduction
Serial Computational :-
• Traditionally,
software has been written for serial computation: To be run on
a single computer having a single Central Processing Unit (CPU)
• Problem is broken into discrete SERIES of instructions.
• Instructions are EXECUTED one after another.
• One instruction may execute at any moment in TIME

5. Introduction
Serial Computational :-

6. Parallel Architecture

7. Definition:
• parallel computing: is the simultaneous use of
multiple compute resources to solve a computational
problem To be run using multiple CPUs.
In which:-
- A problem is broken into discrete parts that can be
solved concurrently
- Each part is further broken down to a series of
instructions
- Instructions from each part execute simultaneously
on different CPUs

8. Definition:

9. Concepts and Terminology:
General Terminology
• Task – A logically discrete section of
computational work
• Parallel Task – Task that can be executed
by multiple processors safely
• Communications – Data exchange
between parallel tasks
• Synchronization – The coordination of
parallel tasks in real time

10. Benefits & Advantages:
• Save Time & Money
• Solve Larger Problems

11. How To Distinguishing Parallel
processors:
– Resource Allocation:
• how large a collection?
• how powerful are the elements?
• how much memory?
– Data access, Communication and Synchronization
• how do the elements cooperate and communicate?
• how are data transmitted between processors?
• what are the abstractions and primitives for cooperation?
– Performance and Scalability
• how does it all translate into performance?
• how does it scale?

12. Multiprocessor Architecture
Classification :
• Distinguishes multi-processor architecture by instruction and
data:-
• SISD – Single Instruction, Single Data
• SIMD – Single Instruction, Multiple Data
• MISD – Multiple Instruction, Single Data
• MIMD – Multiple Instruction, Multiple Data

13. Flynn’s Classical Taxonomy:
SISD
• Serial
• Only one instruction
and data stream is
acted on during any
one clock cycle

14. Flynn’s Classical Taxonomy:
SIMD
• All processing units
execute the same
instruction at any
given clock cycle.
• Each processing unit
operates on a
different data
element.

15. Flynn’s Classical Taxonomy:
MISD
• Different instructions
operated on a single
data element.
• Very few practical uses
for this type of
classification.
• Example: Multiple
cryptography algorithms
attempting to crack a
single coded message.

16. Flynn’s Classical Taxonomy:
MIMD
• Can execute different
instructions on
different data
elements.
• Most common type of
parallel computer.

17. Parallel Computer Memory Architectures:
Shared Memory Architecture
• All processors access
all memory as a
single global address
space.
• Data sharing is fast.
• Lack of scalability
between memory and
CPUs

18. Parallel Computer Memory Architectures:
Distributed Memory
• Each processor has
its own memory.
• Is scalable, no
overhead for cache
coherency.
• Programmer is
responsible for many
details of
communication
between processors.

19. Parallel Programming

20. Parallel Programming Models
• Exist as an abstraction above hardware and
memory architectures
• Examples:
– Shared Memory
– Threads
– Messaging Passing
– Data Parallel

21. Parallel Programming Models:
Shared Memory Model
• Appears to the user as a single shared memory,
despite hardware implementations
• Locks and semaphores may be used to control
shared memory access.
• Program development can be simplified since there
is no need to explicitly specify communication
between tasks.

22. Parallel Programming Models:
Threads Model
• A single process may have
multiple, concurrent
execution paths.
• Typically used with a shared
memory architecture.
• Programmer is responsible
for determining all
parallelism.

23. Parallel Programming Models:
Message Passing Model
• Tasks exchange data by sending
and receiving messages. Typically
used with distributed memory
architectures.
• Data transfer requires cooperative
operations to be performed by each
process. Ex.- a send operation
must have a receive operation.
• MPI (Message Passing Interface) is
the interface standard for message
passing.

24. Parallel Programming Models:
Data Parallel Model
• Tasks performing the
same operations on a set
of data. Each task
working on a separate
piece of the set.
• Works well with either
shared memory or
distributed memory
architectures.

25. Designing Parallel Programs:
Automatic Parallelization
• Automatic
– Compiler analyzes code and identifies
opportunities for parallelism
– Analysis includes attempting to compute
whether or not the parallelism actually
improves performance.
– Loops are the most frequent target for
automatic parallelism.

26. Designing Parallel Programs:
Manual Parallelization
• Understand the problem
– A Parallelizable Problem:
• Calculate the potential energy for each of several
thousand independent conformations of a
molecule. When done find the minimum energy
conformation.
– A Non-Parallelizable Problem:
• The Fibonacci Series
– All calculations are dependent

27. Designing Parallel Programs:
Domain Decomposition
Each task handles a portion of the data set. •

28. Designing Parallel Programs:
Functional Decomposition
Each task performs a function of the overall work •

29. Conclusion
• Parallel computing is fast.
• There are many different approaches and
models of parallel computing.
• Parallel computing is the future of
computing.

30. References
• A Library of Parallel Algorithms, www-
2.cs.cmu.edu/~scandal/nesl/algorithms.html
• Internet Parallel Computing Archive, wotug.ukc.ac.uk/parallel
• Introduction to Parallel Computing,
www.llnl.gov/computing/tutorials/parallel_comp/#Whatis
• Parallel Programming in C with MPI and OpenMP, Michael J. Quinn,
McGraw Hill Higher Education, 2003
• The New Turing Omnibus, A. K. Dewdney, Henry Holt and
Company, 1993

31. Case Study
Developing Parallel Applications
On the Web
using
Java mobile agents and Java threads

32. My References :
• Parallel Computing Using JAVA Mobile
Agents
By: Panayiotou Christoforos, George Samaras ,Evaggelia
Pitoura, Paraskevas Evripidou
• An Environment for Parallel Computing
on Internet Using JAVA
By:P C Saxena, S Singh, K S Kahlon